ALBERT

Description: Selective catalytic reduction (SCR) is coming into worldwide use for diesel engine emissions reduction of on- and off-highway vehicles. These applications are characterized by broad operating range as well as rapid and unpredictable changes in operating condition. Significant nonlinearity, input, and output constraints, and stringent performance requirements have led to the proposal of many different advanced control strategies. This article introduces a model predictive feedback controller based on a nonlinear, reduced order model. Computational effort is significantly reduced through successive linearization, analytical solutions, and a varying terminal cost function. A gradient-based parameter adaptation law is employed to achieve consistent performance. The controller is demonstrated in simulation for an on-highway heavy-duty diesel engine over two widely different emissions test cycles and for 24 different plants. Comparisons with baseline control designs reveal the attractive features as well as the limitations of this approach.

Description: Batteries are the key technology for enabling further mobile electrification and energy storage. Accurate prediction of the state of the battery is needed not only for safety reasons, but also for better utilization of the battery. In this work we present a state estimation strategy for a detailed electrochemical model of a lithium-ion battery. The benefit of using a detailed model is the additional information obtained about the battery, such as accurate estimates of the internal temperature, the state of charge within the individual electrodes, overpotential, concentration and current distribution across the electrodes, which can be utilized for safety and optimal operation. Based on physical insight, we propose an output error injection observer based on a reduced set of partial differential-algebraic equations. This reduced model has a less complex structure, while it still captures the main dynamics. The observer is extensively studied in simulations and validated in experiments for actual electric-vehicle drive cycles. Experimental results show the observer to be robust with respect to unmodeled dynamics as well as to noisy and biased voltage and current measurements. The available state estimates can be used for monitoring purposes or incorporated into a model based controller to improve the performance of the battery while guaranteeing safe operation.

Description: This paper presents both offline and online optimization techniques for the control of a multiple high voltage direct current link power system. A frequency control scheme based on classical proportional-integral-derivative controllers is proposed and optimally tuned offline using particle swarm optimization. The performance of this scheme is compared with the performance of a centralized model predictive control (MPC) scheme, and a distributed MPC scheme that uses only local communications. The results illustrate that a significant performance improvement can be achieved using distributed MPC instead of classical control, illustrating the potential of distributed MPC for use in future power networks.

Description: Solid oxide fuel cells (SOFCs) offer a number of advantages beyond those of most other fuel cells. However, like other fuel cells, rapid load following is difficult, and can lead to fuel starvation and consequently fuel cell damage. Mitigating fuel starvation and improving load following capabilities are conflicting control objectives. However, the issue can be addressed by the hybridization of the system with an energy storage device. In this paper, a steady-state property of the SOFC, combined with a current regulation strategy, is used to manage transient fuel utilization and thereby address fuel starvation. Meanwhile, an overall system strategy is employed to manage energy sharing in the hybrid system for load following as well as for maintaining the state-of-charge of the energy storage device. This work presents an adaptive control algorithm that uses online parameter estimation to update the controller. The control design is validated on a hardware-in-the-loop setup and experimental results are provided.

Description: The problem addressed here is motivated by distributed control for frequency regulation in the electric power grid, and by the characteristics of new technologies contributing to this control objective: wind generation and battery energy storage. In the large scale, coupled dynamical system of the power grid, we seek a distributed control design approach that can successfully share control effort among two classes of actuators: one class having low bandwidth, but broader actuation limits (controllable power output from wind turbines); and a second class, having narrow actuation limits, essentially zero gain at dc, but much broader bandwidth actuation possible at high frequencies (power output from battery energy storage). In this context, we extend the “saturation-respecting” design methodology developed by Saberi and his co-workers, adapting their low-high gain method with partitioning of slow acting actuator input channels (e.g., wind turbine power changes) from fast acting actuators (battery power delivery). The design methodology, resulting frequency regulation performance, and characteristics of control actuation from individual wind generators and batteries is demonstrated in representative test power system models.

Description: This article presents the authors' perspectives on the transfer of research results in control to industrial practice. The distinction between the immediate recipient of a technology development and the end user or beneficiary is drawn and the notion of customer value chains for control applications explained. We argue that successful technology transfer in control requires awareness of current practices in the targeted industry sector, highlighting three critical areas: control development processes, automation system platforms, and work roles. Compatibility challenges in these areas need to be addressed and often overcome, on the basis of criteria that matter to customers. We cite examples from several application domains to contrast industry-specific requirements. Substantial economic and societal impact can be gained from advanced control research with due awareness of customer needs and industry practices.

Description: This paper proposes an automatic control framework to address the problem of distributed and opportunistic transmission power control in wireless communication networks. The proposed framework allows high flexibility on the quality of service (QoS) provision by exploiting the link quality variation. It associates a standard target tracking algorithm with a dynamic target QoS determined by a linear quadratic regulator (LQR) controller that induces an opportunistic behavior. Strict QoS requirements can also be fulfilled by choosing appropriately the weighting parameters of the LQR synthesis. To prevent performance degradation due to measurement uncertainties, robust solutions are developed. The proposed mixed ${cal H}_{2}/{cal H}_{infty}$ distributed power control combines the optimal ${cal H}_{2}$ control and the robust ${cal H}_{infty}$ control to obtain trade-off solutions. All developed algorithms present low computational complexity, and the mixed ${cal H}_{2}/{cal H}_{infty}$ distributed power control can operate as the pure ${cal H}_{2}$ control if the robust constraint is relaxed according to the choice of a single parameter. The performance of proposed algorithms is assessed through computer simulations.

Description: The CyberCarpet is an actuated platform that allows unconstrained locomotion of a walking user for Virtual Reality exploration. The platform consists of a linear treadmill covered by a ball-array carpet and mounted on a turntable, and is equipped with two actuating devices for linear and angular motion. The main control objective is to keep the walker close to the platform center in the most natural way, counteracting his/her voluntary motion while satisfying perceptual constraints. The motion control problem for this platform is not trivial since the system kinematics is subject to a nonholonomic constraint. In the first part of the paper we describe the kinematic control design devised within the CyberWalk project, where the linear and angular platform velocities are used as input commands and feedback is based only on walker's position measurements obtained by an external visual tracking system. In particular, we present a globally stabilizing control scheme that combines a feedback and a feedforward action, based on a disturbance observer of the walker's intentional velocity. We also discuss possible extensions to acceleration-level control and the related assessment of dynamic issues affecting a walker during his/her motion. The second part of the paper is devoted to the actual implementation of the overall system. As a proof of concept of a final full-scale platform, the mechanical design and realization of a small-scale prototype of the CyberCarpet is presented, as well as the visual localization method used to obtain the human walker's position on the platform by an overhead camera. To validate the proposed motion control design, experimental results are reported and discussed for a series of motion tasks performed using a small tracked vehicle representative of a moving user.

Description: This paper deals with the nonlinear control of a jacketed fed-batch three-phase slurry catalytic reactor to track a reference temperature trajectory. To this end, the so-called thermal availability function is defined from the availability function as it has been described in the literature. This thermal availability is used as a Lyapunov function in order to derive a stabilizing control law for the reactor by using the jacket temperature as the manipulated variable. This control is coupled to a high gain observer that is used to estimate the reaction rate that is considered as a time varying parameter. The o-cresol hydrogenation reaction is taken as a representative test example. A detailed model of the process as well as a simplified one that is used for the control law synthesis are described. The performance of the control scheme in presence of measurements noise and parameter uncertainty is illustrated by simulations results.

Description: A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional-integral-derivative control scheme and a feedback state controller.

Description: In this paper, robust adaptive control with dynamic control allocation is proposed for the positioning of marine vessels equipped with a thruster assisted mooring system, in the presence of parametric uncertainties, unknown disturbances and input nonlinearities. Using neural network approximation and variable structure based techniques in combination with backstepping and Lyapunov synthesis, the positioning control is developed to handle the uncertainties, input saturation and dead-zone characteristics of the mooring lines and thrusters. Full state feedback with all states measurable and output feedback using high gain observer to estimate unmeasurable states are considered. Dynamic control allocation is presented for actuation of the position mooring system. Under the proposed robust adaptive control, semi-global uniform boundedness of the closed-loop signals are guaranteed. Numerical simulations are carried out to show the effectiveness of the proposed control.

Description: The experimental study of two kinds of electrical circuits, a domino ladder and a nested ladder, is presented. While the domino ladder is known and already appeared in the theory of fractional-order systems, the nested ladder circuit is presented in this article for the first time. For fitting the measured data, a new approach is suggested, which is based on using the Mittag-Leffler function and which means that the data are fitted by a solution of an initial-value problem for a two-term fractional differential equation. The experiment showed that in the frequency domain the domino ladder behaves as a system of order 0.5 and the nested ladder as a system of order 0.25, which is in perfect agreement with the theory developed for their design. In the time domain, however, the order of the domino ladder is changing from roughly 0.5 to almost 1, and the order of the nested ladder is changing in a similar manner, from roughly 0.25 to almost 1; in both cases, the order 1 is never reached, and both systems remain the systems of non-integer order less than 1. Both studied types of electrical circuits provide the first known examples of circuits, which are made of passive elements only and which exhibit in the time domain the behavior of variable order.

Description: Parametric roll resonance on ships is a nonlinear phenomenon where waves encountered at twice the natural roll frequency can bring the vessel dynamics into a bifurcation mode and lead to extreme values of roll. Recent years have seen several incidents with dramatic damage to container vessels. The roll oscillation, which is subharmonic with respect to the wave excitation, may be completely unexpected and a system for detection of the onset of such resonance could warn the navigators before roll angles reach serious levels. Timely warning could make remedial actions possible, such as change the ship's speed and course, to escape from the bifurcation condition. This paper proposes nonparametric methods to detect the onset of roll resonance and demonstrates their performance. Theoretical conditions for parametric resonance are revisited and are used to develop efficient methods to detect its onset. Spectral and temporal correlations of the square of roll with pitch (or heave) are demonstrated to be of particular interest as indicators. Properties of the indicators are scrutinized, and a change detector is designed for the Weibull-type of distributions that were observed from a time-domain indicator for phase correlation. Hypothesis testing for resonance is developed using a combination of detectors to obtain robustness. Conditions of forced roll and disturbances in real weather conditions are analyzed and robust detection techniques are suggested. The efficacy of the methodology is shown on experimental data from model tests and on data from a container ship crossing the Atlantic during a storm.

Description: In this work, we design a distributed supervisory model predictive control (MPC) system for optimal management and operation of distributed wind and solar energy generation systems integrated into the electrical grid to facilitate the development of the so-called “smart electrical grid”. We consider a topology in which two spatially distributed energy generation systems, a wind subsystem and a solar subsystem, are integrated in a DC power grid, providing electrical power to a local area, and each subsystem is coupled with an energy storage device. A supervisory MPC optimization problem is first formulated to take into account optimality considerations on system operation and battery maintenance; then a sequential and an iterative distributed supervisory MPC architectures are developed to coordinate the actions of the subsystems accordingly. Simulations of 24-hour system operation are carried out under the different control architectures to demonstrate the applicability and effectiveness of the distributed supervisory predictive control design.

Description: The production of polyacrylonitrile carbon fiber (PANCF) consists of a set of industrial processes with high complexity. A key process is the coagulation of the PAN as-spun fiber in the coagulation bath which has a lot of variables coupling with each other. In this paper, an intelligent cooperative decoupling controller (ICDC) based on the neuroendocrine regulation principle of human body is proposed and applied to the coagulation bath in the PANCF production. Three important variables including the liquid-level, the temperature, and the concentration are considered. The ICDC consists of a control center unit, several control and decoupling units and their corresponding output units. The control center coordinates each control and decoupling unit and determines their control schemes. Each control and decoupling unit takes effect independently, and the control information is exchanged among them for decoupling. The control signals are then integrated at the output units and finally sent to the plant. Simulation results demonstrate that the ICDC can rapidly response to the variation of control variables, completely eliminate the coupling influence and make smooth regulation without overshoot, which has a better performance than the conventional control schemes.

Description: An important issue in the trends of miniaturization of systems-on-chips (SoCs) is to obtain a high energy efficiency. This can be reached by dynamic voltage scaling (DVS) architectures as the novel discrete Vdd-Hopping circuit. Generally, this kind of systems present parameter uncertainties and delays. Likewise, current peaks and energy dissipation must be reduced. In this paper, an optimal and robust saturated control law is proposed for this Vdd-Hopping circuit via Lyapunov-Krasovskii theory that ensures asymptotic stability as well as system robustness with respect to delay presence and parameter uncertainties. The closed-loop system presents a regional stabilization due to the actuator saturation. An estimation of an attraction domain is provided. This controller also limits the current peaks and it provides an energy-aware performance. The advantages achieved with this controller are shown in simulation.

Description: A model-based approach for the idle speed control of ultra-lean burn engines is presented. The results from model predictive control (MPC) are extended and collectively used with the existing sampled data control theory to obtain a rigorously developed idle speed control strategy. Controller is designed using MPC theory and facilitated by successive online linearizations of the nonlinear discrete-time model at each sampling instant. Simultaneously, the approximations due to the discretization of the nonlinear engine model are explicitly considered by designing the control within a previously proposed control design framework to obtain appropriate stability guarantees of an exact (unknown) discrete-time engine model. The proposed idle speed control method is experimentally validated on a prototype 6-cylinder hydrogen engine.

Description: This brief presents a data-driven constrained norm-optimal iterative learning control framework for linear time-invariant systems that applies to both tracking and point-to-point motion problems. The key contribution of this brief is the estimation of the system's impulse response using input/output measurements from previous iterations, hereby eliminating time-consuming identification experiments. The estimated impulse response is used in a norm-optimal iterative learning controller, where actuator limitations can be formulated as linear inequality constraints. Experimental validation on a linear motor positioning system shows the ability of the proposed data-driven framework to: 1) achieve tracking accuracy up to the repeatability of the test setup; 2) minimize the rms value of the tracking error while respecting the actuator input constraints; 3) learn energy-optimal system inputs for point-to-point motions.

Description: Many biaxial contouring systems involve competing control objectives of maximizing accuracy while minimizing traversal time. A model predictive controller for contouring systems is proposed where the control inputs are determined by minimizing a cost function which reflects the tradeoff between these competing objectives, subject to state and actuator constraints. The path speed is automatically adjusted to maintain accuracy along the path, and the cost function can be tuned towards higher contouring accuracy and lower path speed, or vice versa. To facilitate real-time implementation, a linear time-varying approach is proposed. The controller is successfully implemented in real time on an X-Y table, and experimental results demonstrate that the new contouring control scheme achieves an improvement in performance compared to both industry standard and advanced tracking controllers.

Description: This work develops a virtual rider that can be used to make a multi-body two-wheeled vehicle follow a specified ground path with a prescribed velocity profile. The virtual rider system is based on a simplified motorcycle model, the sliding plane motorcycle, which is composed of a single rigid body with two ground contact points. This reduced order nonlinear system was presented in an earlier work, together with a dynamic inversion procedure for computing a state-control trajectory corresponding to the desired task. This dynamic inversion procedure is combined in this work with a maneuver regulation controller to yield a nonlinear feedback control strategy. A transverse coordinate system that is consistent with the mechanical symmetries of ground vehicles is constructed and used in the development of the maneuver regulation controller. An inverse optimal control strategy, which also exploits the mechanical symmetries, is developed to shape the dynamic response of the closed loop system. Numerical results with the virtual rider driving a multi-body vehicle through a demanding maneuver with lateral accelerations reaching 1 g are presented.

Description: A repetitive control method is presented that is implemented in real-time for periodic wind disturbance rejection for linear systems with multiple inputs and multiple outputs and with both repetitive and non-repetitive disturbance components. The novel repetitive controller can reject the periodic wind disturbances for fixed-speed wind turbines and variable-speed wind turbines operating above-rated and we will demonstrate this on an experimental “smart” rotor test section. The “smart” rotor is a rotor where the blades are equipped with a number of control devices that locally change the lift profile on the blade, combined with appropriate sensors and controllers. The rotational speed of wind turbines operating above-rated will vary around a defined reference speed, therefore methods are given to robustify the repetitive controllers for a mismatch in the period. The design of the repetitive controller is formulated as a lifted linear stochastic output-feedback problem on which the mature techniques of discrete linear control may be applied. For real-time implementation, the computational complexity can be reduced by exploiting the structure in the lifted state-space matrices. With relatively slow changing periodic disturbances it is shown that this repetitive control method can significantly reduce the structural vibrations of the “smart” rotor test section. The cost of additional wear and tear of the “smart” actuators are kept small, because a smooth control action is generated as the controller mainly focuses on the reduction of periodic disturbances.

Description: In this brief, data-rate assignment in IS-856 uplink (reverse link) is studied. The problem is first formulated using an interference model, and then a dynamic control strategy is developed for efficient rate assignment. In the first step, the controller is designed for the special case when the number of users in the network is fixed. Then, the designed controller is further developed for a dynamic network (where the number of users is subject to change) to achieve the desired performance. To this end, the network is formulated in the framework of switched systems, where any new activation or deactivation of users is considered as switching from one system to another. The controllers obtained are then modified properly to retain network stability and performance in the presence of time-delay. Simulation results are presented to elucidate the effectiveness of the proposed approach.

Description: Lists the recipients of the 2012 IEEE Transactions on Control Systems Technology Outstanding Paper award.

Description: In a recent paper a procedure to design globally asymptotically stabilizing linear proportional plus integral controllers for switched power converters was proposed. The construction requires the measurement of the full state of the system, which is often unavailable in practice. In this note we identify a class of converters for which an asymptotically convergent reduced order observer, preserving the aforementioned stability property of the closed-loop, can be designed. The class is characterized by a simple linear matrix inequality. The new controller is illustrated with the widely-popular, and difficult to control, single-ended primary inductor converter, for which simulation and experimental results are presented.

Description: In this brief, we consider the formation control problem of underactuated autonomous surface vehicles (ASVs) moving in a leader-follower formation, in the presence of uncertainties and ocean disturbances. A robust adaptive formation controller is developed by employing neural network and dynamic surface control technique. The stability of the design is proven via Lyapunov analysis where semiglobal uniform ultimate boundedness of the closed-loop signals is guaranteed. The advantages of the proposed formation controller are that: first, the proposed method only uses the measurements of line-of-sight range and angle by local sensors, no other information about the leader is required for control implementation; second, the developed neural formation controller is able to capture the vehicle dynamics without exact information of coriolis and centripetal force, hydrodynamic damping and disturbances from the environment. Comparative analysis with a model-based approach is given to demonstrate the effectiveness of the proposed method.

Description: Offline robot dynamic identification methods are mostly based on the use of the inverse dynamic model, which is linear with respect to the dynamic parameters. This model is sampled while the robot is tracking reference trajectories that excite the system dynamics. This allows using linear least-squares techniques to estimate the parameters. The efficiency of this method has been proved through the experimental identification of many prototypes and industrial robots. However, this method requires the joint force/torque and position measurements and the estimate of the joint velocity and acceleration, through the bandpass filtering of the joint position at high sampling rates. The proposed new method called DIDIM requires only the joint force/torque measurement, which avoids the calculation of the velocity and acceleration by bandpass filtering of the measured position. It is a closed-loop output error method where the usual joint position output is replaced by the joint force/torque. It is based on a closed-loop simulation of the robot using the direct dynamic model, the same structure of the control law, and the same reference trajectory for both the actual and the simulated robot. The optimal parameters minimize the 2-norm of the error between the actual force/torque and the simulated force/torque. This is a nonlinear least-squares problem which is dramatically simplified using the inverse dynamic model to obtain an analytical expression of the simulated force/torque, linear in the parameters. A validation experiment on a two degree-of-freedom direct drive rigid robot shows that the new method is efficient.

Description: We study a disturbance rejection problem in a production pulsed light source, used in semiconductor photolithography, to yield quantifiable and guaranteed improved performance over existing control techniques. The disturbances of interest include an offset with reset properties and sinusoids which appear aliased in the measured data which is available only at pulse events. The light source is pulsed at varying rates yet actuators move in continuous-time, yielding a system which blends aspects of continuous-time and variable-data-rate discrete-time. We employ novel modifications to standard continuous-discrete Kalman filtering ideas for disturbance state estimation and establish and solve a non-standard regularized minimum variance control problem within a disturbance rejection framework. The controller as discussed is now in production in semiconductor lithography manufacturing lines. We analyze data from these production light sources and show the controller has the capacity to remove aliased sinusoids from the measured output and yields operational performance levels provably close to optimal for the hardware.

Description: Contour control is an important task for many motion control applications. This paper reviews the state-of-the-art control methods in academic research for contour tracking, including individual axis tracking control, cross coupled control and its variants, and other contour control methods, such as neural networks and velocity field control. Different methods for contour error estimation are also reviewed. Areas for future multiaxis contour control research are discussed.

Description: The detection of direct causality, as opposed to indirect causality, is an important and challenging problem in root cause and hazard propagation analysis. Several methods provide effective solutions to this problem when linear relationships between variables are involved. For nonlinear relationships, currently only overall causality analysis can be conducted, but direct causality cannot be identified for such processes. In this paper, we describe a direct causality detection approach suitable for both linear and nonlinear connections. Based on an extension of the transfer entropy approach, a direct transfer entropy (DTE) concept is proposed to detect whether there is a direct information flow pathway from one variable to another. Especially, a differential direct transfer entropy concept is defined for continuous random variables, and a normalization method for the differential direct transfer entropy is presented to determine the connectivity strength of direct causality. The effectiveness of the proposed method is illustrated by several examples, including one experimental case study and one industrial case study.

Description: In order for photovoltaic (PV) systems to maximize their efficiency of power generation, it is crucial to locate the maximum power point (MPP) in real time under realistic illumination conditions. The current–voltage $(Ihbox{--}V)$ characteristics of PV devices are nonlinear, and the MPP may vary with intrinsic and environmental conditions. Maximum power point tracking (MPPT) control is expected to seek the MPP regardless of the device and ambient changes. This brief presents the application of the adaptive extremum seeking control (AESC) scheme to the PV MPPT problem. A state-space model is derived via averaging method, with the control input being the duty ratio of the pulse-width modulator of the dc–dc buck converter. To address the nonlinear PV characteristics, the radial basis function neural network is used to approximate the unknown nonlinear $(Ihbox{--}V)$ curve. The convergence of the system to an adjustable neighborhood of the optimum is guaranteed by utilizing a Lyapunov-based adaptive control method. The performance of the AESC is verified with simulation.

Description: Actuator arrays are planar arrangements of actuators with two degrees of freedom that cooperatively translate and orient objects for efficient manipulation. This brief describes a trajectory tracking control law for macroscopic actuator arrays and its convergence properties. The control law is designed on the basis of the kinematics of an object on the array. Its convergence properties are found using a direct solution of the Kalman–Yakubovich–Popov equations. Simulations show that the control law is effective and that the bounds are useful for objects under no-slip contact. Experimental results from a 20-unit prototype array demonstrate the controller performance.

Description: From a practical–theoretic viewpoint, there is a need to develop rigorous design and analysis tools for the control, fault diagnosis, and security of wastewater quality. However, sludge bulking remains a widespread problem in the operation of activated sludge processes, which leads to severe economic and environmental consequences. Sludge volume index (SVI) monitoring is a key challenge that will become even more crucial in the years ahead to quantify sludge bulking. This brief presents a system that consists of online sensors and an SVI predicting plant. The SVI predicting plant uses a hierarchical radial basis function (HRBF) neural network to predict SVI in a wastewater treatment process (WWTP). Then, an approach named extended extreme learning machine (EELM) is proposed for training the weights of HRBF. Unlike conventional single-hidden-layer feedforward networks, this EELM-HRBF is based on the hierarchical structure which is capable of hierarchical learning of sequential information online, and one may only need to adjust the output weights linking the hidden and the output layers. In such EELM-HRBF implementations, the EELM provides better generalization performance during the learning process. Moreover, the convergence of the proposed algorithm is analyzed. To illustrate the methodology, the proposed predicting plant with the EELM-HRBF has been tested and compared with other methods by applying it to the problem of predicting SVI in a simplified and real WWTP. Experimental results show that the EELM-HRBF can be used to predict the wastewater quality online. The results demonstrate its effectiveness.

Description: Computer hardware is moving from uniprocessor to multicore architectures. One problem arising in this evolution is that only parallel software can exploit the full performance potential of multicore architectures, and parallel software is far harder to write than conventional serial software. One important class of failures arising in parallel software is circular-wait deadlock in multithreaded programs. In our ongoing Gadara project, we use a special class of Petri nets, called Gadara nets, to systematically model multithreaded programs with lock allocation and release operations. In this paper, we propose an efficient optimal control synthesis methodology for ordinary Gadara nets that exploits the structural properties of Gadara nets via siphon analysis. Optimality in this context refers to the elimination of deadlocks in the program with minimally restrictive control logic. We formally establish a set of important properties of the proposed control synthesis methodology, and show that our algorithms never synthesize redundant control logic. We conduct experiments to evaluate the efficiency and scalability of the proposed methodology, and discuss the application of our results to real-world concurrent software.

Description: This paper extends the algorithms used to fit standard support vector machines (SVMs) to the identification of auto-regressive exogenous (ARX) input Hammerstein models consisting of a SVM, which models the static nonlinearity, followed by an ARX representation of the linear element. The model parameters can be estimated by minimizing an $varepsilon$ -insensitive loss function, which can be either linear or quadratic. In addition, the value of the uncertainty level, $varepsilon$ , can be specified by the user, which gives control over the sparseness of the solution. The effects of these choices are demonstrated using both simulated and experimental data.

Description: For current diesel engines, multiple fuel injection mechanisms enabled by high rail-pressure systems is a key lever that can help to achieve further reduction in engine-out emissions and improvements in performance. In the case of multiple fuel injections, timing and fuel pulse-width for each pulse (or, equivalently, fuel amount) need to be optimized and maintained for low emissions, fuel economy, noise, and exhaust thermal management over different operating ranges. This paper presents a research study on the application of pressure-based controls for management of the multiple-pulse fuel injection, particularly main and post injections, to maintain a robust combustion behavior against disturbances and variations in the field. Several control features for simultaneous management of main and post injections are proposed and experimentally validated on a 6.6 L V8 diesel engine in an engine dynamometer, both at steady-state and during federal test procedure transients.

Description: This paper addresses the problem of fault detection (FD) in nonlinear stable systems, which are monitored via communications networks. An FD based on the system data provided by the communications network is called networked fault detection (NFD) or over network FD in the literature. A residual signal is generated, which gives a satisfactory estimation of the fault. A sufficient condition is derived, which minimizes the estimation error in the presence of packet drops, quantization error, and unwanted exogenous inputs such as disturbance and noise. A linear matrix inequality is obtained for the design of the FD filter parameters. In order to produce appropriate fault alarms, two widely used residual signal evaluation methodologies, based on the signals' peak and average values, are presented and compared together. Finally, the effectiveness of the proposed NFD technique is extensively assessed by using an experimental testbed that was built for performance evaluation of such systems with the use of IEEE 802.15.4 wireless sensor networks (WSNs) technology. In particular, this paper describes the issue of floating point calculus when connecting the WSNs to the engineering design softwares, such as MATLAB, and a possible solution is presented.

Description: Powered orthoses are external mechanical devices used to stabilize human limbs, to restore or to reinforce lost or weak functions of people with reduced mobility. The embodied actuators produce the necessary joint torques to compensate gravity and passive effort as well as to generate the intended human movements. Nonlinearities due to human orthosis coupling, as well as modeling errors, parameter uncertainties, and external disturbances, necessitate the use of a robust closed-loop controller in order to guarantee precise movement generation. This paper aims to present a new prototype of an actuated knee joint orthosis using a robust controller. This orthosis is designed to restore or to assist knee-joint movements of dependent people. Dynamic modeling of the lower limb/orthosis is presented, and its parameters are estimated using different techniques. Control strategies based on second-order sliding mode are applied, which show satisfactory performance compared to classical controllers in terms of tracking errors and robustness with respect to parameter uncertainties and external disturbances. Real-time experiments are conducted on healthy subjects to illustrate the efficiency of the proposed approach.

Description: Moving-horizon estimation provides a general method for state estimation with strong theoretical convergence properties under the critical assumption that global solutions are found to the associated nonlinear programming problem at each sampling instant. A particular benefit of the approach is the use of a moving window of data that is used to update the estimate at each sampling instant. This provides robustness to temporary data deficiencies such as lack of excitation and measurement noise, and the inherent robustness can be further enhanced by introducing regularization mechanisms. In this paper, we study moving-horizon estimation in cases when output measurements are lost or delayed, which is a common situation when digitally coded data are received over low-quality communication channels or random access networks. Modifications to a basic moving-horizon state estimation algorithm and conditions for exponential convergence of the estimation errors are given, and the method is illustrated by using a simulation example and experimental data from an offshore oil drilling operation.

Description: A nonlinear position tracking controller with a disturbance observer (DOB) is proposed to track the desired position in the presence of the disturbance for electrohydraulic actuators (EHAs). The DOB is designed in the form of a second-order high-pass filter in order to estimate the disturbance. The nonlinear controller is designed for position tracking as a near input–output linearizing inner-loop load pressure controller and a backstepping outer-loop position controller. Variable structure control is implemented in order to compensate for the error in disturbance estimation. The desired load pressure is designed to generate the pressure using the differential flatness property of the EHA's mechanical subsystem. The disturbance within the bandwidth of the DOB can be cancelled by the proposed method. The performance of the proposed method is validated via simulations and experiments.

Description: With increasing populations of the elderly in our society, robot technology will play an important role in providing functional mobility to humans. From the perspective of human safety, it is desirable that controllers for walk-assist robots be dissipative, i.e., the energy is supplied from the human to the walker, while the controller modulates this energy. The simplest form of a dissipating controller is a brake, where resistive torques are applied to the wheels proportional to their speeds. The fundamental question that we ask in this brief is how to modulate these proportional gains over time for the two wheels so that the walker can perform point-to-point motions. The unique contribution of this brief is a novel way in which the theory of differential flatness is used to plan the trajectory of these braking gains. Since the user input forces are not known a priori , the trajectory of the braking gain is computed iteratively during the motion. Simulation and experimental results show that the walk-assist robot, along with the structure of this proposed control scheme, can guide the user to reach the goal.

Description: This paper studies the problem of traction control, i.e., how to stabilize a wheeled mobile robot (WMR) subject to wheel slippage to a desired configuration. The WMR is equipped with a rechargeable battery pack which powers electric drives on each wheel. The drives propel the WMR in one mode of operation or recharge the battery pack (recover energy) in a second mode. These modes of operation are controlled, whereas wheel slippage, e.g., due to ice, is an autonomous mode change. The WMR can be thus modeled as a hybrid system with both controlled and autonomous switches. Model predictive control (MPC) for such systems, although robust, typically results in numerical methods of combinatorial complexity. We show that recently developed embedding techniques can be used to formulate numerical algorithms for the hybrid MPC problem that have the same complexity as MPC for smooth systems. We also discuss the numerical techniques that lead to efficient and robust MPC algorithms in detail. Simulations illustrate the effectiveness of the approach.

Description: Maintaining the connectivity of an underlying robot network during a rendezvous task in the presence of obstacles is a challenge in control systems technology. In this brief, a navigation-function-based potential field approach is developed to address this challenging problem. A concept called connectivity constraint is used to establish a navigation function. A new potential field that simultaneously integrates rendezvous requirement, connectivity maintenance, and obstacle avoidance is also developed. On the basis of this potential field, we design a bounded control input for multirobot control. The proposed controller can drive multiple robots to an agreement state while maintaining connectivity of the underlying network provided that the initial configurations of the robots are connected. Simulations and experiments are performed to verify the effectiveness of the proposed approach.

Description: In this brief, a new control scheme is presented for the doubly fed induction machine (DFIM). The proposed control algorithm offers the advantages of proven stability and remarkable simplicity. In contrast to the classical vector control method, where the DFIM is represented in a stator-flux-oriented frame, a model with orientation of the stator voltage is adopted. This approach allows the decomposition of the active and reactive powers on the stator side and their regulation on the rotor side. A main contribution of this brief is the use of the Hurwitz test for polynomials with complex coefficients that has had little prior application in control theory. This results in a proof that a proportional-integral (PI) control regulating the stator currents ensures global stability for a feedback-linearized DFIM. The specific condition that the PI gains must satisfy is derived as a simple inequality. The PI controller has a particular structure that directly relates the $d$ -component of the rotor voltages to the $q$ -component of the stator currents and vice versa. The feedback linearization stage only uses the direct measurement of the rotor and stator currents and is thus easily implementable. Furthermore, it is also shown that the PI controller (without the feedback linearization terms) is also stable for a large range of control gains and does not require the knowledge of the machine parameters. Finally, the control system is validated in simulations and in experiments.

Description: Engine knock is an undesirable phenomenon, which requires feedback control in order to maximize engine efficiency and avoid damage to the engine. In this paper, an analysis of experimental data is used to provide further evidence that knock behaves as a cyclically uncorrelated random process. It is argued that all knock controllers are therefore ultimately stochastic in nature and that the knock control problem is best undertaken within a stochastic framework. The properties of knock events are discussed and, based on these properties, a new likelihood-based stochastic knock controller is presented. The new controller achieves a significantly improved regulatory response relative to conventional strategies, while also maintaining a rapid transient response. It is therefore possible to operate closer to the knock limit without increasing the risk of engine damage.

Description: In this brief, variable structure systems theory based guidance laws, to intercept maneuvering targets at a desired impact angle, are presented. Choosing the missile's lateral acceleration (latax) to enforce sliding mode, which is the principal operating mode of variable structure systems, on a switching surface defined by the line-of-sight angle leads to a guidance law that allows the achievement of the desired terminal impact angle. As will be shown, this law does not ensure interception for all states of the missile and the target during the engagement. Hence, additional switching surfaces are designed and a switching logic is developed that allows the latax to switch between enforcing sliding mode on one of these surfaces so that the target can be intercepted at the desired impact angle. The guidance laws are designed using nonlinear engagement dynamics for the general case of a maneuvering target.

Description: Path following entails having the output of a control system approach a path and traverse it without a priori time parameterization of the motion along the path. We design path-following controllers for a class of mechanical control systems applicable to closed and nonclosed paths. Our approach uses transverse feedback linearization (TFL) to partially meet our objective by putting the system into a convenient normal form for control design. To meet the remaining control requirements, we present a method of refining the TFL normal form. Our approach is demonstrated experimentally on a five-bar robotic manipulator and in simulation on a nonminimum phase robotic manipulator with a flexible link.

Description: In this brief, a controller is proposed in order to avoid the parametric resonance effect and to attenuate the load oscillations in a parametrically excited crane, ensuring precise load transfer during the load movement despite model uncertainties and un-modeled dynamic actuators. The nonlinear controller proposed in this brief is motivated by the Twisting algorithm and the design uses the vector Lyapunov function approach ensuring the ultimate bounded stability of the overall closed-loop system. The experiments conducted over a laboratory platform resemble quite well the simulations, confirming the obtained results.

Description: This brief addresses two estimation problems relevant to traction control for motorcycles: longitudinal vehicle velocity estimation and wheelie (i.e., front wheel lifting off the ground during acceleration) detection. Two methods to estimate the vehicle body velocity are discussed and compared: a complementary filter and a Kalman filter. The Kalman filter reduces the noise affecting the estimate of the longitudinal vehicle velocity by an order of magnitude without introducing any phase lag. Furthermore, a wheelie detection algorithm is developed. The approach is based on the fault detection paradigm and detects wheelies in 70 ms. Both methods are computationally efficient and industrially viable. Track tests on an instrumented sport motorcycle are employed to illustrate and validate the methods.

Description: This brief proposes modified projective synchronization (MPS) methods for underactuated unknown heavy symmetric chaotic gyroscope systems via optimal Gaussian radial basis adaptive variable structure control. Chaotic gyroscope systems are considered as underactuated systems where a control input is designed to synchronize the two degree of freedoms interactions. Until now, no investigation of this subject with one control input has been presented. The importance of obtaining synchronization objectives is specified when the dynamics of gyroscope system are unknown. In this brief, using the neural variable structure control technique, a control law is established that guarantees the MPS of underactuated unknown chaotic gyros. In the neural variable structure control, Gaussian radial basis functions are utilized to estimate online the system dynamic functions. Adaptation laws of the online estimator are derived in the sense of the Lyapunov function. Moreover, online and offline optimizers are applied to optimize the energy of the control signal. The proposed solution is generalized to chaos control of the mentioned gyroscopes. Numerical simulations are presented to verify the proposed synchronization methods.

Description: The rolling resistance and the effective radius of a vehicle's tires are two important characteristics that affect its dynamics, performance, and comfort. Because of their dependence on tire inflation pressure, online estimation of such parameters could be used to monitor tire pressures using an indirect approach. By considering rotational and longitudinal dynamics, the aim of this paper proposes to apply observers for this online estimation using measurements of the wheels' angular velocities and the engine torque. Because these signals are available on major vehicle controller area networks, the proposed solutions do not require additional sensors. These nonlinear observers are based, first, on a high-gain approach, and then on a high-order sliding-mode approach, allowing robustness and finite time convergence. The originality of the presented results consists in providing a joint estimation of both variables, i.e., wheel effective radius and rolling resistance force. The observers offer the very first solution for dynamical estimation of rolling resistance in standard driving conditions, but the rolling resistance is very difficult to estimate by an online procedure. Simulations and experimental results allow the discussion of the effect of tire pressure on these parameters and illustrate the applicability of the proposed approach.

Description: This brief presents a reactive reference conditioning algorithm for robot collision avoidance based on geometric invariance and sliding-mode (SM) ideas. First, constraints are defined in terms of the measurements given by the robot's sensors in order to guarantee that collisions will not occur. Then, a supervisory loop ensures the fulfillment of the constraints modifying the reference trajectory as much as necessary by means of a discontinuous control law. The proposed algorithm activates only when the constraints are about to be violated and, thus, in contrast to conventional SM approaches, there exists no reaching mode to the limit surface of the constraints (sliding surface). The validity and effectiveness of the proposed approach is substantiated by simulation and experimental results using a mobile robot equipped with infrared sensors.

Description: A control scheme is developed to address the attitude tracking problem of a rigid spacecraft. A sufficient condition to accommodate actuator misalignment is presented. The controller asymptotically stabilizes the closed-loop attitude tracking system. The desired attitude trajectories can be tracked in finite time, even in the presence of uncertain inertia tensor, external disturbances, actuator fault, and actuator misalignment. The attitude tracking performance of the controller is evaluated through an illustrative example.

Description: This brief presents the design of an optimal wind energy control system for maximum power point tracking. With the help of a grey predictor for the preprocessor, a high-performance online training Elman neural network (ENN) is designed to derive the turbine speed needed to extract maximum power from wind. Moreover, the connective weights of the improved ENN are trained online by the backpropagation learning algorithm. Compared to earlier methods, better results are obtained when the ENN controller is used together with the grey system modeling approach. Performance of the proposed approach is verified by the experimental results.

Description: This paper investigates the problem of vibration suppression in vehicular active suspension systems, whose aim is to stabilize the attitude of the vehicle and improve the riding comfort. A full-car model is adopted, and electrohydraulic actuators with highly nonlinear characteristics are considered to form the basis of accurate control. In this paper, the $H_{infty}$ performance is introduced to realize the disturbance suppression by selecting the actuator forces as virtual inputs, and an adaptive robust control technology is further used to design controllers which help real force inputs track virtual ones. The resulting controllers are robust against both actuator parametric uncertainties and uncertain actuator nonlinearities. The stability analysis for the closed-loop system is given within the Lyapunov framework. Finally, a numerical example is given to illustrate the effectiveness of the proposed control law, where different road conditions are considered in order to reveal the closed-loop system performance in detail.

Description: Biologically inspired snake robots have been widely studied for their various motion patterns. Most research has focused on the design of a controller for a given motion pattern. However, relatively limited work appears to have been done on the design of a controller for self-adaptive locomotion. In this brief, we add sensory inputs to a control system in order to investigate collision avoidance in a snake robot using a neural controller based on central pattern generator. From an analysis of the steering mechanism during serpentine locomotion, we derive a mathematical model of the joint configuration and the steering angle. In a neural oscillator network, steering control can be achieved via the proposed amplitude modulation method by modulating the neural oscillation parameters. A head-navigated motion pattern is employed to allow the range sensors to accurately detect obstacles for collision avoidance. Through the head-navigated locomotion, the head of the snake robot can be controlled to keep the orientation the same as the motion direction. The proposed control method is experimentally verified by application to the SR-I snake robot.

Description: We apply an operator splitting technique to a generic linear-convex optimal control problem, which results in an algorithm that alternates between solving a quadratic control problem, for which there are efficient methods, and solving a set of single-period optimization problems, which can be done in parallel, and often have analytical solutions. In many cases, the resulting algorithm is division-free (after some off-line pre-computations) and can be implemented in fixed-point arithmetic, for example on a field-programmable gate array (FPGA). We demonstrate the method on several examples from different application areas.

Description: In this paper, a centralized control model for optimal management and operation of a smart network of microgrids (SNMs) is designed. The proposed control strategy considers grid interconnections for additional power exchanges. This paper is based on an original Linear Quadratic Gaussian (LQG) problem definition for the optimal control of power flows in a SNMs. The control strategy incorporates storage devices, various distributed energy resources, and loads. The objective function aims to minimize the power exchanges among microgrids (MGs), and to make each local energy storage system in a MG works around a proper optimal value. The proposed model is evaluated through a case study in the Savona district, Italy, consisting of four MGs that cooperate together under an SNMs connected to a main grid. The case study shows that the proposed approach can effectively cope with the aim to decrease the intermittencies effects of renewable energy sources, and to manage real-time burst in the residential local demands.

Description: In this paper, we introduce a design framework for variable gain integral controllers with the aim to improve transient performance of linear motion systems. In particular, we focus on the well-known tradeoff introduced by integral action, which removes steady-state errors caused by constant external disturbances, but may deteriorate transient performance in terms of increased overshoot. We propose a class of variable gain integral controllers (VGICs), which limits the amount of integral action if the error exceeds a certain threshold, in order to balance this tradeoff in a more desirable manner. The resulting nonlinear controller consists of a loop-shaped linear controller with a variable gain element. The utilization of linear controllers as a basis for the control design appeals to the intuition of motion control engineers therewith enhancing the applicability. For the add-on part of the nonlinear variable gain part of the controller, we propose an optimization strategy, which enables performance-optimal tuning of the variable gain based on measurement data. The effectiveness of VGIC is demonstrated in practice on a high-precision industrial scanning motion system.

Description: Sensor networks must be able to fuse nodes’ perceptions in a reliable way to reach a trustworthy consensus. Data association mistakes and measurement outliers are some of the factors that can contribute to incorrect perceptions and considerably affect consensus values. In this paper, we present a novel distributed scheme for robust consensus in autonomous sensor networks. The proposed method builds on random sampling consensus to exploit measurement redundancy, and enables the network to determine outlier observations with local communications. To do this, different hypotheses are generated and voted for using distributed averaging. In our approach, nodes can change their opinion as the hypotheses are computed, making the voting process dynamic. Assuming that enough hypotheses are generated to have at least one composed exclusively by inliers, we show that the method converges to the maximum likelihood of all the inlier observations under some natural conditions. We present several simulations and examples with real information that demonstrate the good performance of the proposed algorithm.

Description: This paper presents the use of robotically controlled optical tweezers to move a group of biological cells into a desired region for required patterning. A multilevel-based topology is designed to present different cell patterning in the desired region. A potential function-based controller is developed to control the cells in forming the required patterning as well as achieving rotation and scaling of the desired patterning. A pattern regulatory control force is additionally designed and added to the cell patterning controller for particularly addressing the local minima problem that causes the cells to stop at the undesired positions. The stability of the controlled system is analyzed using a direct Lyapunov approach. Experiments are performed with a cell manipulation system equipped with holographic optical tweezers to demonstrate the effectiveness of the proposed approach.

Description: Currently, braking on board subsystems such as wheel slide protection (WSP) devices almost totally control the longitudinal train dynamics. In particular, the vehicle safety highly depends on the study and the development of these systems, especially at high speeds and under degraded adhesion conditions. Usually, to save time and to avoid expensive on-track tests, the performances of braking subsystems are tested on full-scale roller-rigs. Nevertheless, the analysis of the subsystem behavior under degraded adhesion conditions is still limited to a few applications on roller-rigs because large slidings among the rollers and wheelsets produce severe wear of the rolling surfaces. This circumstance is not acceptable due to the effects on the maintenance costs (the rollers have to be turned or substituted), on the system dynamical stability and on the safety. In this paper, the modeling and control of an innovative hardware in the loop (HIL) architecture to test braking on board subsystems on full-scale roller-rigs is described. The new approach permits to reproduce on the roller-rig a generic wheel-rail adhesion pattern and, in particular, degraded adhesion conditions. The presented strategy is also followed by the innovative full-scale roller-rig of the Railway Research and Approval Center of Firenze-Osmannoro (Italy); the new roller-rig has been built by Trenitalia and is owned by SIMPRO. At this initial phase of the research activity, to effectively validate the proposed approach, a complete model of the HIL system has been developed. The complete numerical model is based on the real characteristics of the components provided by Trenitalia. The results coming from the simulation model have been compared with the experimental data provided by Trenitalia and relative to on-track tests performed in Velim, Czech Republic, with a UIC-Z1 coach equipped with a fully working WSP system. The preliminary validation performed with the HIL model highlights the good performance - f the HIL strategy in reproducing on the roller-rig, the complex interaction between the degraded adhesion conditions and railway vehicle dynamics during the braking maneuver.

Description: Multirobot collaboration has great potentials in tasks, such as reconnaissance and surveillance. In this paper, we propose a multirobot system that integrates reinforcement learning and flocking control to allow robots to learn collaboratively to avoid predator/enemy. Our system can conduct concurrent learning in a distributed fashion as well as generate efficient combination of high-level behaviors (discrete states and actions) and low-level behaviors (continuous states and actions) for multirobot cooperation. In addition, the combination of reinforcement learning and flocking control enables multirobot networks to learn how to avoid predators while maintaining network topology and connectivity. The convergence and scalability of the proposed system are investigated. Simulations and experiments are performed to demonstrate the effectiveness of the proposed system.

Description: A complete methodology to design robust fault detection and isolation (FDI) filters and fault-tolerant control (FTC) schemes for linear parameter varying systems is proposed, with particular focus on its applicability to wind turbines. This paper takes advantage of the recent advances in model falsification using set-valued observers (SVOs) that led to the development of FDI methods for uncertain linear time-varying systems, with promising results in terms of the time required to diagnose faults. An integration of such SVO-based FDI methods with robust control synthesis is described, to deploy new FTC algorithms that are able to stabilize the plant under faulty environments. The FDI and FTC algorithms are assessed by resorting to a publicly available wind turbine benchmark model, using Monte Carlo simulation runs.

Description: The control design for bilateral teleoperation systems represents a challenge in finding the proper balance in the inherent tradeoff between transparency and stability. To address this problem, we propose a methodology in which we develop a parametric model of the teleoperation system. Subsequently, we exploit robust control techniques based on linear matrix inequalities to design controllers that aim to achieve a predefined performance, and are robust to bounded but arbitrarily fast-time-varying parametric uncertainties. We present analysis, simulation, and experimental results of the designed controller, thus showing that the assumptions made during modeling are appropriate and the effectiveness of the method to tradeoff perfect transparency and stability.

Description: For over 30 years, many active and passive antipendulation concepts have been explored for use on cranes in the marine environment. These range from simple tension member restraints to command filtering strategies and advanced feedback control laws, where both measured ship/platform motion and payload swing are required. Single crane control systems that compensate for own ship or target ship motion and payload swing damping are well developed, and have been demonstrated. Cargo transfer control is more complex when multiple ship-mounted cranes are used, representing a closed kinematic chain. However, the potential benefits include larger capacity and better load control. In this brief, an inverse kinematic control strategy is presented that uses two cranes' actuation capability (hoist lengths and boom angles) to keep its load fixed in inertial space regardless of the motion of the ship on which the cranes are mounted. An underdetermined solution is developed. Unique crane commands can then be computed using a minimum norm solution. A dynamic simulation is described for use in algorithm development and initial validation. Final verification was performed using two cranes mounted on a motion controlled platform.

Description: Adaptive optics (AO) provide real-time compensation for atmospheric turbulence to improve the resolution of images acquired by ground-based optical telescopes. The actuators in deformable mirrors, which are used as correctors, are constrained by a maximal allowable movement. The control techniques used in current AO systems do not account for these constraints, leading to inferior performance and a risk of damage of the deformable mirror’s surface. This paper presents a feasibility study for receding horizon control (RHC) with online constrained quadratic programming (QP). The results of numerical simulations provided in this paper are based on realistic models obtained from an optical test-bench. We compare QP algorithms that represent three main methods for convex optimization: 1) interior point; 2) active set (AS); and 3) gradient-based algorithms. It is shown that constrained RHC is computationally feasible for moderate-size AO systems using hot-started structure-exploiting AS QP solvers with bound constraints. An evaluation of performance indicates that RHC is advantageous in terms of atmospheric turbulence rejection in the case of active constraints.

Description: Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X–Y table and the F-16 aircraft.

Description: Formation control analysis and design problems for unmanned aerial vehicle (UAV) swarm systems to achieve time-varying formations are investigated. To achieve predefined time-varying formations, formation protocols are presented for UAV swarm systems first, where the velocities of UAVs can be different when achieving formations. Then, consensus-based approaches are applied to deal with the time-varying formation control problems for UAV swarm systems. Necessary and sufficient conditions for UAV swarm systems to achieve time-varying formations are proposed. An explicit expression of the time-varying formation center function is derived. In addition, a procedure to design the protocol for UAV swarm systems to achieve time-varying formations is given. Finally, a quadrotor formation platform, which consists of five quadrotors is introduced. Theoretical results obtained in this brief are validated on the quardrotor formation platform, and outdoor experimental results are presented.

Description: The fast-growing technology of large scale wind turbines demands control systems capable of enhancing both the efficiency of capturing wind power, and the useful life of the turbines themselves. $ell _{{1}}$ -Optimal control is an approach to deal with persistent exogenous disturbances which have bounded magnitude ( $ell _{{infty }}$ -norm) such as realistic wind disturbances and turbulence profiles. In this brief, we develop an efficient method to compute the $ell _{{1}}$ -norm of a system. As the control synthesis problem is nonconvex, we use the proposed method to design the optimal output feedback controllers for a linear model of a wind turbine at different operating points using genetic algorithm optimization. The locally optimized controllers are interpolated using a gain-scheduled technique with guaranteed stability. The controller is tested with comprehensive simulation studies on a 5 MW wind turbine using fatigue, aerodynamics, structures, and turbulence (FAST) software. The proposed controller is compared with a well-tuned proportional-integral (PI) controller. The results show improved power quality, and decrease in the fluctuations of generator torque and rotor speed.

Description: Dynamic reconfigurability is receiving more and more attention from both academy and industry, which means the ability to flexibly modify system functions by adding/removing hardware/software components, modifying logic relation between components, or updating particular system data at runtime without sacrificing the system performance. A distributed reconfigurable discrete event control system (DRDECS) is composed of several networked reconfigurable subsystems. In order to realize system functions, these reconfigurable subsystems communicate and coordinate with each other, since any casually reconfiguration applied to a subsystem may cause risks to others, or even to the safety of the whole system. This brief proposes a new coordination method for a DRDECS, where each subsystem is modeled by a reconfigurable timed net condition/event system. A virtual coordinator together with a communication protocol between it and subsystems is developed in order to achieve two aims: 1) to coordinate subsystems with an optimal coordination solution using judgement matrices while multiple subsystems require global reconfigurations and 2) to reduce exchanged messages between the coordinator and these subsystems. Furthermore, for the purpose of checking functional and temporal properties of a DRDECS with this virtual coordinator, a computation tree logic-based model checking method is applied. Finally, a hypothetic manufacturing plant is used as a running example to illustrate this brief.

Description: This brief presents the design, analysis, and verification of a new scheme of digital sliding mode prediction control (DSMPC) for precise position control of piezoelectric micro/nanopositioning systems. Its implementation only needs input/output measurements, whereas the burdens on hysteresis modeling and state observer design are released. The robustness against piezoelectric nonlinearities and model disturbances is guaranteed by a devised digital sliding mode control (DSMC). As compared with DSMC, the DSMPC is capable of further attenuating the positioning error through an optimal control, which is provided by the predictive control strategy. Its stability is proved and ultimate tracking error bounds are evaluated analytically. The feasibility of the control scheme is validated by experimental investigations on a piezo-driven micropositioning device. Results exhibit that the DSMPC surpasses proportional-integral-derivative control and DSMC in terms of high-speed motion tracking accuracy, which is afforded by an increased bandwidth.

Description: In this brief, we introduce a graph rigidity-based, adaptive formation control law for multiple robotic vehicles moving on the plane that explicitly accounts for the vehicle dynamics while allowing for parametric uncertainty. We consider a class of vehicles modeled by Euler-Lagrange-like equations of motion. The control is designed via backstepping, and exploits rigid graph theory and the structural properties of the system dynamics. A Lyapunov analysis shows that the desired formation is acquired asymptotically. A five-vehicle simulation is used to illustrate the proposed formation acquisition control.

Description: Flight control systems are often equipped with reconfigurable control allocation schemes to redistribute control commands in the event of actuator failures. In this brief, a reconfiguration scheme is proposed and is based on the direct allocation method. An iterative algorithm is developed and uses the same lookup tables prepared offline for normal operations, thereby saving a considerable amount of computation time and process memory. To elucidate the effectiveness of the method, the proposed allocation algorithm is applied to a satellite launch vehicle model. Simulation results show satisfactory performance of the method in the presence of multiple actuator failures.

Description: In this brief, a new tracking control algorithm for a class of networked control systems (NCSs) with non-Gaussian random disturbances and delays is proposed. Due to non-Gaussian random noises involved in the systems, solely controlling the expected value of the linear quadratic performance index is insufficient to obtain a satisfactory optimal control algorithm. The proposed method in this note applies the $( {h,phi } )-$ entropy of the quadratic performance index to characterize the randomness of the closed-loop system. In order to calculate the entropy, the formulation of the joint probability density functions (JPDFs) of the quadratic performance index is presented in terms of known JPDFs of disturbances and time-delay. By minimizing the entropy of the performance index, a new control algorithm is obtained for the considered nonlinear and non-Gaussian NCSs. In addition, the proposed control strategy is applied to a networked DC motor control system, which is subjected to non-Gaussian random disturbances and delays. The experimental results show the effectiveness of the obtained method.

Description: The state-space explosion problem, resulting from the reachability computations in controller synthesis, is one of the main obstacles preventing supervisory control theory from having an industrial breakthrough. To alleviate this problem, a strategy is to symbolically perform the synthesis procedure using binary decision diagrams. Based on this principle, the work presented in this brief develops an efficient symbolic reachability approach for discrete event systems that are modeled as finite automata with variables, referred to as extended finite automata. Using a disjunctive event partitioning technique, the proposed approach first partitions the transition relation of the considered system into a set of partial transition relations. These partial transition relations are then selected systematically to perform the reachability analysis, which is the most fundamental challenge for synthesizing supervisors. It has been shown through solving a set of benchmark supervisory control problems for EFA that the proposed approach significantly improves scalability in comparison with the previously published results.

Description: A major difficulty encountered in the application of linear parameter-varying (LPV) control is the complexity of synthesis and implementation when the number of scheduling parameters is large. Often heuristic solutions involve neglecting individual scheduling parameters, such that standard LPV controller synthesis methods become applicable. However, stability and performance guarantees are rendered void, if controller designs based on an approximate model are implemented on the original plant. In this brief, a synthesis method for LPV controllers that achieves reduced implementation complexity is proposed. The method is comprised of first synthesizing an initial controller based on a reduced parameter set. Then closed-loop stability and performance guarantees are recovered with respect to the original plant, which is considered to be accurately modeled. Iteratively solving a nonconvex bilinear matrix inequality may further improve performance. A two-degrees-of-freedom (2-DOF) and three-degrees-of-freedom robotic manipulator is considered as an illustrative example, for which experimental results indicate a good performance for controllers of reduced scheduling order. Furthermore, in the 2-DOF case, controller performance has been significantly improved.

Description: An industrial case study involving a large-scale hydraulic network underlying a district heating system subject to structural changes is considered. The problem of controlling the pressure drop across the so-called end-user valves in the network to a designated vector of reference values under directional actuator constraints is addressed. The proposed solution consists of a set of decentralized positively constrained proportional control actions. The results show that the closed-loop system always has a globally asymptotically stable equilibrium point independently on the number of end-users. Furthermore, by a proper design of controller gains the closed-loop equilibrium point can be designed to belong to an arbitrarily small neighborhood of the desired equilibrium point. Since there exists a globally asymptotically stable equilibrium point independently on the number of end-users in the system, it is concluded that structural changes can be implemented without risk of introducing instability. In addition, structural changes can be easily implemented due to the decentralized control architecture.

Description: This paper presents a solution to the problem of trajectory tracking control for autonomous surface craft (ASC) in the presence of ocean currents. The proposed solution is rooted in nonlinear model predictive control (NMPC) techniques and addresses explicitly state and input constraints. Whereas state saturation constraints are added to the underlying optimization cost functional as penalties, input saturation constraints are made intrinsic to the nonlinear model used in the optimization problem, thus reducing the computational burden of the resulting NMPC algorithm. Simulation results, obtained with a nonlinear dynamic model of a prototype ASC, show that the NMPC strategy adopted yields good performance in the presence of constant currents. Experimental results are also provided to validate the real-time implementation of the NMPC techniques for ASCs.

Description: This index covers all technical items - papers, correspondence, reviews, etc. - that appeared in this periodical during the year, and items from previous years that were commented upon or corrected in this year. Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name. The primary entry includes the co-authors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination. The Subject Index contains entries describing the item under all appropriate subject headings, plus the first author's name, the publication abbreviation, month, and year, and inclusive pages. Note that the item title is found only under the primary entry in the Author Index.

Description: In the field of human–robot interaction in industrial environments, the active control of robot based on exteroceptive sensors' measurements is a viable approach to the issue of safety enhancement. Among all possible solutions, onboard sensors have several advantages, in terms of ease of deployment and calibration, and absence of occlusions. In this paper, we present a prototype of a distributed distance sensor that can be mounted on an industrial robot. The sensor's outputs have been used as part of a newly conceived control strategy, aimed at improving human safety by means of assessing the level of danger induced by the robot. Several experiments on an ABB IRB140 industrial robot have been carried out, demonstrating the feasibility of the proposed approach in a realistic scenario.

Description: Extremum seeking (ES) is a real-time optimization technique that has been applied to maximum power point tracking (MPPT) design for photovoltaic (PV) microconverter systems, where each PV module is coupled with its own dc/dc converter. Most of the existing MPPT designs are scalar, i.e., employ one MPPT loop around each converter, and all designs, whether scalar or mutivariable, are gradient based. The convergence rate of gradient-based designs depends on the Hessian, which in turn is dependent on environmental conditions, such as irradiance and temperature. Therefore, when applied to large PV arrays, the variability in environmental conditions and/or PV module degradation results in nonuniform transients in the convergence to the maximum power point (MPP). Using a multivariable gradient-based ES algorithm for the entire system instead of a scalar one for each PV module, while decreasing the sensitivity to the Hessian, does not eliminate this dependence. We present a recently developed Newton-based ES algorithm that simultaneously employs estimates of the gradient and Hessian in the peak power tracking. The convergence rate of such a design to the MPP is independent of the Hessian, with tunable transient performance that is independent of environmental conditions. We present simulation as well as the experimental results that show the effectiveness of the proposed algorithm in comparison with the existing scalar designs, and also to multivariable gradient-based ES.

Description: This paper proposes a novel approach for camera-based modeling and control for a large class of continuous systems and the validity is confirmed by liquid sloshing experiments. It is an unsolved problem to design a model-based control in nonplanar sloshing cases. This is because the whole shape of the liquid surface is a complex curve (a set of an infinite number of points) in coordinate spaces. This paper solves this problem. First, the whole shape of the liquid surface corresponding to the output measured by a camera is a single point in a polynomial space as well as the input and the state. Second, without any physical parameter identification, input–output modeling on polynomial space, unlike existing types of modeling, captures the whole dynamics even in nonplanar sloshing cases and is linked to design of implementable controllers. Finally, in the presence of occlusion, the nonplanar sloshing is controlled well by state estimation on polynomial space without adding any image processing technology.

Description: This paper describes robust multi-input, multi-output controller for the exhaust gas recirculation (EGR) and variable geometry turbocharger (VGT) systems of passenger car diesel engines. The air-to-fuel ratio of the exhaust gas and boost pressure of the intake manifold were selected as performance indicators in this paper. To enable the online calibration of the controller, proportional–integral–derivative (PID) was used as a feedback controller. Using quantitative feedback theory (QFT), two control loops for air-to-fuel ratio and boost pressure were independently designed with linearized models and parameter uncertainties. The prefilters and PID gains of two control loops were designed for satisfying required robust stability and tracking performance using the QFT design framework. Furthermore, the problems originated from the cross-coupled dynamics between the EGR and VGT systems were mitigated by a static decoupler. Using the proposed design steps, PID and decoupler gains of the representative 15 engine operating points which are mainly used in New European Driving Cycle were obtained. The proposed controller was validated through various test conditions of engine experiments. From the step responses and transient experiments, it was demonstrated that the required robustness and tracking performance were successfully achieved.

Description: We consider the speed control of a spark ignition engine during vehicle deceleration. When the torque converter bypass clutch is open, the engine speed needs to be kept close to the turbine speed to guarantee responsiveness of the vehicle for subsequent accelerations. However, to maintain vehicle drivability, undesired crossing between engine speed and turbine speed must not occur, despite the presence of significant torque disturbances. Hence, the engine speed during vehicle decelerations needs to be precisely controlled by feedback control, which has to coordinate airflow and spark timing and enforce several constraints including engine stall avoidance, combustion stability, and actuator limits. We develop a model predictive controller that manipulates airflow and spark to track the reference signal for engine speed while enforcing constraints, and synthesize it in the form of a feedback law. The controller is evaluated in simulations and in a vehicle, and it is shown to achieve a responsive and consistent deceleration and the potential for reducing fuel consumption.

Description: This paper aims to develop a new data-driven ${cal H}_{infty}$ norm estimation algorithm for model-error modeling of multivariable systems. An iterative approach is presented that requires significantly a fewer prior assumptions on the true system, hence it provides stronger guarantees in a robust control design. The iterative estimation algorithm is embedded in a robust control design framework with a judiciously selected uncertainty structure to facilitate high control performance. The approach is experimentally implemented on an industrial active vibration isolation system.

Description: This paper presents a new state-space model interpolation of local estimates technique to compute linear parameter-varying (LPV) models for parameter-dependent systems using a set of linear time-invariant models obtained for fixed operating conditions. The technique is based on observability and controllability properties and has three strong appeals, compared with the state of the art in the literature. First, it works for continuous-time as well as discrete-time multiple-input multiple-output systems depending on multiple scheduling parameters. Second, the technique is automatic to some extent, in the sense that, after the model selection, no user interaction is required at the different steps of the method. Third, the resulting interpolating LPV model is numerically well-conditioned such that it can be used for modern LPV control design. Moreover, the proposed technique guarantees that the local models have a coherent state-space representation encompassing existing results as a particular case. The benefits of the approach are demonstrated on a simulation example and on an experimental data set obtained from a vibroacoustic setup.

Description: Stochastic distribution control (SDC) systems are known to have the 2-D characteristics regarding time and probability space of a random variables, respectively. A double closed-loop structure, which includes iterative learning modeling (ILM) and iterative learning control (ILC), is proposed for non-Gaussian SDC systems. The ILM is arranged in the outer loop, which takes a longer period for each cycle termed as a BATCH. Each BATCH is divided into a modeling period and a number of control intervals, called batches, being arranged in the inner loop for ILC. The output probability density functions (PDFs) of the system are approximated by a radial basis function neural network (RBFNN) model, whose parameters are updated via ILM in each BATCH. Based on the RBFNN approximation of the output PDF, a state-space model is constructed by employing the subspace parameter estimation method. An IL optimal controller is then designed by decreasing the PDF tracking errors from batch to batch. Model simulations are carried out on a forth-order numerical example to examine the effectiveness of the proposed algorithm. To further assess its application feasibility, a flame shape distribution control simulation platform for a combustion process in a coal-fired gate boiler system is constructed by integrating WinCC interface, MATLAB simulation programs, and OPC communication together. The simulation study over this industrial simulation platform shows that the output PDF tracking performance can be efficiently achieved by this double closed-loop iterative learning strategy.

Description: This paper addresses two interrelated problems concerning the planar three degree-of-freedom motion of a vehicle, namely, the path planning problem and the guidance problem. The monotone cubic Hermite spline interpolation (CHSI) technique by Fritsch and Carlson is employed to design paths that provide the user with better shape control and avoid wiggles and zigzags between the two successive waypoints. The conventional line-of-sight (LOS) guidance law is modified by proposing a time-varying equation for the lookahead distance, which is a function of the cross-track error. This results in a more flexible maneuvering behavior that can contribute to reaching the desired path faster as well as obtaining a diminished oscillatory behavior around the desired path. The guidance system along with a heading controller form a cascaded structure, which is shown to be $kappa$ -exponentially stable when the control task is to converge to the path produced by the aforementioned CHSI method. In addition, the issue of compensating for the sideslip angle $beta$ is discussed and a new $kappa$ -exponentially stable integral LOS guidance law, capable of eliminating the effect of constant external disturbances for straight-line path following, is derived.

Description: This brief deals with the design and experimental application of a hybrid fractional adaptive cruise control (ACC) at low speeds. First, an improved fractional-order cruise control (CC) is presented for a commercial Citroën C3 prototype—which has automatic driving capabilities—at low speeds, which considers a hybrid model of the vehicle. The quadratic stability of the system is proved using a frequency domain method. Second, ACC maneuvers are implemented with two different distance policies using two cooperating vehicles—one manual, the leader, and the other, automatic—also at very low speeds. In these maneuvers, the objective is to maintain a desired interdistance between the leader and follower vehicles, i.e., to perform a distance control—with a proportional differential (PD) controller in this case—in which the previously designed fractional-order CC is used for the speed control. Simulation and experimental results, obtained in a real circuit, are given to demonstrate the effectiveness of the proposed control strategies.

Description: In this brief, we study robust pulse design for electron shuttling in solid-state devices. This is crucial for many practical applications of coherent quantum mechanical systems. Our objective is to design control pulses that can transport an electron along a chain of donors and that also make this process robust to parameter uncertainties. We formulate this problem here as a set of optimal control problems and derive explicit expressions for the gradients of the aggregate transfer fidelity. Numerical results for a donor chain of ionized phosphorus atoms in bulk silicon demonstrate the efficacy of our algorithm.

Description: In this brief, we address the robust force regulation problem of mechanical systems in physical interaction with compliant environments. The control method that we present is entirely derived under the energy shaping framework. Note that for compliant interactions, standard energy shaping methods (i.e., potential shaping controls using static-state feedback actions) cannot guarantee asymptotic stability since they are not robust to unmodeled forces. To cope with this issue, in this brief, we integrate force sensory feedback with a robust energy shaping design. This methodology allows us to incorporate integral force controls while preserving in closed loop the port-Hamiltonian structure, something that is not possible with traditional force regulators. We discuss the practical implementation of our method and provide simple numerical algorithms to compute in real time some of its control terms. To validate our approach, we report an experimental study with an open architecture robot manipulator.

Description: A particle filter (PF)-based robust navigation with fault diagnosis (FD) is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switching-mode hidden Markov model. By extensively running a PF on the model, the FD and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods, the proposed method can diagnose all faults within a single structure, it can diagnose simultaneous faults, and it is easily implemented.

Description: We propose a hybrid control system performing set-point regulation of an exhaust gas recirculation valve of a Diesel engine. The control technique is based on a first-order reset element (FORE) embedded with an adaptive feedforward action whose aim is to provide asymptotic rejection of disturbances acting at the plant input. The feedforward action is adapted by suitable resetting laws occurring whenever the FORE is reset to zero. We first provide a formal analysis of the effectiveness of the adaptive reset system to guarantee asymptotic set-point regulation, and then, we illustrate how the adaptive feedforward can be parameterized for improved transient performance. We experimentally illustrate the proposed solution on a Diesel engine testbench, which reveals substantial position accuracy improvement during a standard driving cycle, as compared with the production standard solution.

Description: When a system has to perform a series of point-to-point (PTP) motions within a given time, the execution time for each individual motion is, in many cases, free to choose. This letter presents a dynamic programming algorithm to allocate the time to each motion in a series of PTP motions in an energy-optimal way. The algorithm is demonstrated on an $xy$ -positioning stage and the results are compared with an equal time allocation scheme.

Description: In this paper, a networked estimation framework for a team of relative sensing multiagent systems is proposed. This framework is constructed based on the developed notion of subobservers (SOs). Within a group of SOs, each SO is estimating certain team states that are conditioned on a given input, output, and other state information. The overall team estimation process is then modeled by a weighted estimation (WE) digraph. By selecting an optimal path in the WE digraph, a high-level supervisor provides a decision on the selection and reconfiguration of the set of SOs to successfully estimate all the states of the multiagent system. In presence of unreliable information due to either large disturbances, noise, and actuator faults, or communication delays certain SOs may become invalid and carry a high cost. In this case, the supervisor reconfigures the set of SOs by selecting a new path in the estimation digraph such that the impacts of these unreliabilities are managed and confined. This will consequently prevent the propagation of unreliabilities to the entire estimation process and avoid performance degradations to the multiagent system. Simulation results are conducted for a five spacecraft formation flight system in deep space where the validity and advantages of our analytical developed schemes are confirmed.