ALBERT

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.

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Description: In this paper, performance enhancements for deformable membrane mirrors based on model-based feedforward control are presented. The investigated deformable mirror consists of a flexible membrane and voice coil actuators. Feedback control of the distributed actuators cannot be implemented in these mirrors due to a lack of high speed internal position measurements of the membrane's deformation. However, by using feedforward control, the dominant dynamics of the membrane can still be controlled allowing for faster settling times and reduced membrane vibrations. Experimental results are presented for an ALPAO deformable mirror with 88 distributed actuators on a circular membrane with a pupil of two centimeters in diameter.

Description: In this paper, an observer-based fault diagnostics and prediction (FDP) scheme for a class of nonlinear discrete-time systems via output measurements is introduced by using artificial immune system (AIS) and a robust adaptive term. Traditionally, AIS was considered as an offline tool for system identification and pattern recognition whereas here AIS is utilized as an online approximator in discrete-time (OLAD) in a fault detection (FD) observer. A fault is detected when the output residual exceeds a predefined threshold. Upon detection, the OLAD is initiated to learn the unknown fault dynamics online while the robust adaptive term ensure asymptotic convergence of the output residual for a state fault whereas a bounded result for an output fault. Additionally, a mathematical equation is introduced to estimate the time-to-failure (TTF) by using the output residual and the estimated fault parameters. Finally, the performance of the proposed FDP scheme is demonstrated on an axial piston pump hardware test-bed.

Description: In this paper, a switching control approach for active vibration isolation systems is proposed. The switching involves two regimes. In the first regime, no feedback control is applied thereby giving a low sensitivity to noise. In the second regime, active control induces improved disturbance rejection properties, but at the cost of increased noise sensitivity. Conditions for the stability of the switching closed-loop system are formulated whereas the stability analysis provides design rules for tuning the switching controller. Given this novel active vibration isolation approach, improved isolation performance is obtained with substantially less control authority in comparison to the case of linear (or non-switching) feedback control. Performance analysis is based on multi-resolution time-frequency analysis using measurements taken from a commercial vibration isolation system.

Description: Real-life cyber physical systems, such as automotive vehicles, building automation systems, and groups of unmanned vehicles are monitored and controlled by networked control systems (NCS). The overall system dynamics emerges from the interaction among physical dynamics, computational dynamics, and communication networks. Network uncertainties such as time-varying delay and packet loss cause significant challenges. This paper proposes a passive control architecture for designing NCS that are insensitive to network uncertainties. We describe the architecture for a system consisting of a robotic manipulator controlled by a digital controller over a wireless network and show that the system is stable even in the presence of time-varying delays. Experimental results demonstrate the advantages of the passivity-based architecture with respect to stability and performance and show that the system is insensitive to network uncertainties.

Description: This brief presents an analysis of the time-optimal control for rest-to-rest motion of flexible structures with lightly damped modes. For such systems, the switch command timings are shown to be periodic, and the repeat time of the solution set is derived as a function of the natural mode frequencies. Based on this result, we formulate a new approach for the quick generation of time-optimal commands for any given reference move where a finite set of these periodic solutions are computed offline, and are later used to derive time-optimal profiles by solving a much reduced subset of original problem in real-time. This provides large computational benefit over existing schemes, which require expensive computations where nonconvex and nonlinear parametric optimization problems are solved for every reference move. The proposed approach is compared with existing input shaping-based schemes in terms of computational requirement and system performance. Extensions are provided for systems with multiple flexible modes, and with different limits on maximum acceleration and deceleration.

Description: A new method for the design of network-based control systems, together with mathematical tools for closed-loop stability assessment, is proposed. In order to preserve the closed-loop stability under random network time delays, the variable selective control (VSC) methodology is introduced, which combines the idea of variable sampling control with the packet-based control methodologies. In the proposed method, event-driven sensors are employed, which sample the plant output just after a new control input signal is received by the actuator. The advantages are as follows: 1) the latest actual sampling period becomes equal to the most recent time delay, which is known by direct measurement; 2) better estimation of the actual plant states can be performed in the predictive controller; 3) less frequent and shorter packets need to be transferred through the network; and 4) the stability analysis becomes simpler and does not depend on variable-size matrices. In order to deal with packet dropout issue, a simple, yet effective, algorithm is adopted in the controller, where packet losses are considered as variable time delays. In light of this formulation, the networked control system can be considered as a switched linear system and, therefore, previously existing theoretical results are adopted for stability analysis of the VSC method. Simulation studies on a well-known benchmark problem demonstrate the effectiveness of the proposed method.

Description: This letter addresses the performance study of an adaptive controller in the presence of gain and delay uncertainties. The nonlinearities in the controller presented here do not allow an analytical study of the effect of uncertainties on performance. A numerical study is done instead using the sampling, learning, prediction, and optimization capabilities of the software Global Optimization, Sensitivity and Uncertainty in Models (GoSUM).

Description: Some errors in the above titled paper (ibid., vol. 20, no. 5, Sep. 2012, pp. 1146-1159) are corrected here.

Description: Flexible fuel vehicles (FFVs) can operate on a blend of ethanol and gasoline in any volumetric concentration of up to 85% ethanol (93% in Brazil). Existing FFVs rely on ethanol sensor installed in the vehicle fueling system, or on an ethanol estimation based on air-to-fuel ratio (AFR) regulation via an exhaust gas oxygen (EGO) or $lambda $ sensor. The EGO-based ethanol detection is desirable from cost and maintenance perspectives but it is known to be prone to large errors during mass air flow sensor drifts. Ethanol content estimation can be realized by a feedback-based fuel correction of the feedforward-based fuel calculation using an exhaust gas oxygen sensor. When the fuel correction is attributed to the difference in stoichiometric air-to-fuel ratio (SAFR) between ethanol and gasoline, it can be used for ethanol estimation. When the fuel correction is attributed to a mass air flow (MAF) sensor error, it can be used for sensor drift estimation and correction. Deciding under which condition to blame (and detect) ethanol and when to switch to sensor correction burdens the calibration of FFV engine controllers. Moreover, erroneous decisions can lead to biases in ethanol estimation and in MAF sensor correction. In this paper, we present AFR-based ethanol content estimation, associated sensitivity and dynamical analysis, and a cylinder air flow estimation scheme that accounts for MAF sensor drift or bias using an intake manifold absolute pressure (MAP) sensor. The proposed fusion of the MAF, MAP, and $lambda $ sensor measurements prevents severe misestimation of ethanol content in flex fuel vehicles.

Description: Iterative learning control (ILC) is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. However, in many application domains the output is not critical at all points over the task duration. In this paper the facility to track an arbitrary subset of points is therefore introduced, and the additional flexibility this brings is used to address other control objectives in the framework of iterative learning. These comprise hard and soft constraints involving the system input, output and states. Experimental results using a robotic arm confirm that embedding constraints in the ILC framework leads to superior performance than can be obtained using standard ILC and an a priori specified reference.

Description: We demonstrate for the first time the effectiveness of output feedback controllers previously designed for the Ginzburg-Landau model in attenuating vortex shedding in an actual flow past a 2-D cylinder. The actual flow is represented by a full-blown computational fluid dynamics (CFD) solver, from which measurements are restricted to the cylinder surface, co-located with actuation. Actuation is performed by rotation of the cylinder, and the sensed quantity is the shear at the downstream end of the cylinder. The observer-based controller successfully attenuates vortex shedding, stabilizes the flow, and eliminates lift force oscillations. CFD simulation flow fields are examined in detail, revealing the basic mechanism of the controller and providing an intuitive explanation as to why it works.

Description: Modern gasoline internal combustion engines use a variety of technologies to enhance the efficiency of fresh air induction. These technologies, which include variable valve timing and variable intake geometry systems, also make it more difficult to predict the mass of fresh air that is trapped during the induction stroke of the engine because they not only affect the residual gas fraction of the trapped air charge, but also the wave dynamics of the system. As the number of controllable actuators increases, this estimation problem becomes even more difficult. As these technologies continue to develop, the importance of robustness in air-to-fuel ratio control continues to grow. This paper presents an air-to-fuel ratio control algorithm based on a switching frequency regulator that has favorable robust stability properties in the presence of both input and model errors. Instead of modeling the air path system with a simplified model, this control architecture considers the air estimate as a control input. As a result, air estimation errors behave like input errors, not modeling errors. By using the rich-to-lean and lean-to-rich air-to-fuel ratio switching frequencies of the pre-catalyst exhaust gas oxygen sensor as the primary feedback signal, the control laws are completely independent of the parameters of the plant model. The performance of this controller is demonstrated both with a robust stability analysis and through a vehicle-based experimental validation.

Description: Rhythmic motion employed in animal locomotion is ultimately controlled by neuronal circuits known as central pattern generators (CPGs). It appears that these controllers produce efficient oscillatory command signals by entraining to a resonant gait via sensory feedback. This property is of great interest in the control of autonomous vehicles. In this paper, we experimentally validate synthesized CPG control of tensegrity structures. The prestressed cables in a tensegrity structure provide a method of simultaneous actuation and sensing, analogous to the biological motor control mechanism of regulating muscle stiffness through motoneuron activation and sensing the resulting motion by stretch receptors. A three-cell class-two tensegrity structure is designed, built, and modeled to predict the structure's dynamic response. The models are experimentally validated using open-loop control tests. Next, a simple CPG, called a reciprocal inhibition oscillator (RIO), is designed and synthesized in real time. The RIOs outputs are used as actuation commands, while sensory signals from the tensegrity are fed back to the RIO. Multiple controller configurations are tested to validate an RIO design method developed and reported in a complementary study. Finally, the tensegrity dynamics are perturbed by altering the mass of the tensegrity, and the robustness of RIO control is demonstrated through its ability to entrain to the perturbed system.

Description: Cyclic control (CC) is concerned with systems comprised of multiple plants controlled by a single actuator and sensor. Such systems arise naturally in rotating machinery with actuators and sensors fixed in inertial space. This paper investigates the problem of reference tracking and disturbance rejection in CC systems and illustrates the results through the control of the fusing stage of a xerographic process.

Description: Partly motivated by nanopositioning applications in scanning probe microscopy systems, we consider the problem of tracking periodic signals for a class of systems consisting of linear dynamics preceded by a hysteresis operator, where uncertainties exist in both the dynamics and the hysteresis. A robustified servocompensator is proposed, in combination with an approximate hysteresis inverse, to achieve high-precision tracking. The servocompensator accommodates the internal model of the reference signal and a finite number of harmonic terms. Using a Prandtl–Ishlinskii (PI) operator for modeling hysteresis, we show that the closed-loop system admits a unique and asymptotically stable periodic solution, which justifies treating the inversion error as an exogenous periodic disturbance. Consequently, the asymptotic tracking error can be made arbitrarily small as the servocompensator accommodates a sufficient number of harmonic terms. The analysis is further extended to the case where the hysteresis is modeled by a modified PI operator. Experiments on a commercial nanopositioner show that, with the proposed method, tracking can be achieved for a 200-Hz reference signal with 0.52% mean error and 1.5% peak error, for a travel range of 40 $mu{rm m}$ . The performance of the proposed method in tracking both sinusoidal and sawtooth signals does not fall off with increasing frequency as fast as the proportional-integral controller and the iterative learning controller, both adopted in this paper for comparison purposes. Further, the proposed controller shows excellent robustness to loading conditions.

Description: Adaptive feedforward broadband vibration (or noise) compensation requires a reliable correlated measurement with the disturbance (an image of the disturbance). The reliability of this measurement is compromised in most of the systems by a “positive” internal feedback coupling between the compensator system and the correlated measurement of the disturbance. The system may become unstable, if the adaptation algorithms do not take into account this positive feedback. Instead of using classical infinite impulse response (IIR) or finite impulse response (FIR) feedforward compensators, this paper proposes and analyses an IIR Youla–Kucera parametrization of the feedforward compensator. A model-based central IIR stabilizing compensator is used, and its performance is enhanced by the adaptation of the parameters (Q-parameters) of an IIR Youla–Kucera filter. Adaptation algorithms assuring the stability of the system in the presence of the positive internal feedback are provided. Their performances are evaluated experimentally on an active vibration control system. Theoretical and experimental comparisons with FIR Youla–Kucera parameterized feedforward compensators and IIR feedforward compensators are provided.

Description: This paper addresses the problem of e-safety driving in urban areas, where the principal limiting factor is vehicle-to-vehicle or vehicle-to-infrastructure communications. Time-varying network-induced delays constitute the main concern of networked control systems since they may negatively affect the velocity control of a vehicle at low speeds and consequently cause an accident. A system to adapt the vehicle's speed to avoid or mitigate possible accidents has been developed. In particular, gain scheduling is used in a local fractional-order proportional integral controller to compensate the effects of delay. Experimental results on a prototype Citroën vehicle in a real environment are presented, which demonstrate the effectiveness of the proposed system.

Description: Teleoperated systems may be subject to destabilizing and performance degrading effects due to time delays. An appealing remedy is an application of the internal model principle and control together (IMPACT) structure as it is done in this paper in the problem of position-error-based bilateral teleoperation. The IMPACT algorithm proposed in this paper allows time-delay compensation and rejection of disturbances from a known class that act at the output of the slave manipulator. Simulation and experimental results illustrate the effectiveness of the algorithm.

Description: Parallel working units in closed-loop operation are frequently encountered in industrial applications of advanced process control (boilers, turbines, chemical reactors, etc.). Control strategies typically require different low-order models for each configuration of parallel units. These different models are usually obtained by heuristics applied to the parallel models. To replace these heuristics, this paper proposes a systematic solution based on structured model order reduction. Two methods are considered, the first has general applicability to stable closed-loop systems, but gives no a priori error bounds; the second linear matrix inequality (LMI)-based method comes with an explicit error bounds, but cannot be applied to general models. However, it is shown that for models composed of cascades of stable subsystems and negative feedbacks of strictly positive real subsystems, the LMIs are always feasible. Both methods are demonstrated on a practical example of a cogeneration power plant with multiple boilers. It is proved that the second LMI-based method can always be applied to general problems with structures similar to the boiler-header systems considered in this paper.

Description: A robot belt grinding system provides promising prospects for relieving hand grinders from their noisy work environment, as well as for improving machining accuracy and product consistency. However, for a manufacturing system with a flexible grinder, controlling the robot to perform precise material removal from free-form surfaces is a challenge. In the belt grinding process, material removal is related to a variety of factors, such as workpiece shape, contact force, and robot velocity. Some factors of the grinding process, such as belt wear, are time-variant. To achieve the desired removal in the grinding process, an intelligent control method for the industrial robot is proposed in this paper. First, an adaptive grinding process model that can track discontinuous changes in working conditions is constructed to precisely predict material removal in accordance with in situ measurement data. With incorporated prior knowledge, the method considerably improves model accuracy, which worsens when new samples from an in situ measurement are insufficient or are unevenly distributed under new working conditions. After this, an online trajectory generation method for the robot control parameters is proposed. By calculating the optimal control parameters in real time, the control transition process is shortened and its negative effect on grinding quality is reduced. Finally, the preliminary grinding experiments validate the workability and effectiveness of the proposed control method.

Description: We consider motion planning and feedforward control for a cantilevered flexible plate-like structure actuated by a finite number of surface-mounted piezoelectric patches to realize prescribed highly dynamic trajectories for the deflection profile in open loop. For this, a distributed-parameter mathematical model including damping and localized effects originating from the spatially distributed patch actuators is derived by means of the extended Hamilton's principle. With this, a flatness-based design methodology is proposed for motion planning and feedforward control, which directly exploits the distributed-parameter system description. In particular, differential state, input, and output parameterizations are systematically constructed in terms of a basic output to achieve a one-to-one correspondence between system trajectories. Finite element methods are incorporated into the design to account for structures with nontrivial domain and nonisotropic material behavior. In addition, the convergence of the system parameterization is analyzed analytically and by means of numerical results. Finally, measurement results demonstrate the applicability of this approach for the realization of highly dynamic rest-to-rest transitions of the deflection profile of an orthotropic plate structure with macro-fiber composite patch actuators.

Description: The invariant observer is a recently introduced constructive nonlinear design method for symmetry-possessing systems such as the magnetometer-plus-global positioning system (GPS)-aided inertial navigation system (INS) example considered in this paper. The resulting observer guarantees a simplified form of the nonlinear estimation error dynamics, which can be stabilized by a proper choice of observer gains using a nonlinear analysis. A systematic approach to this step is the invariant Extended Kalman Filter (EKF), which is modified from its originally proposed form and applied to the aided INS example to obtain the observer gains. The resulting invariant observer is implemented onboard an outdoor helicopter unmanned aerial vehicle platform and successfully validated in experiment and demonstrates an improvement in performance over a conventional (non-invariant) EKF design.

Description: The problem of fault detection and isolation/identification (FDI) of nonlinear systems using neural networks is considered in this paper. The proposed FDI approach employs recurrent neural network-based observers for simultaneously detecting, isolating and identifying the severity of actuator faults in presence of disturbances and uncertainties in the model and sensory measurements. The neural network weights are updated based on a modified dynamic backpropagation scheme. The proposed FDI scheme does not rely on the availability of full state measurements. In most works in the literature the fault function acts as an additive term on the actuator, whereas in this work the fault acts as a multiplicative term. This will make the formal stability and convergence analysis of the overall FDI scheme nontrivial and challenging. Our stability analysis considers the presence of plant and sensor uncertainties through the use of Lyapunov's direct method with no restrictive assumptions on the system and/or the FDI algorithm. The performance of our proposed FDI approach is evaluated through simulations that are performed for two case studies, namely FDI of 1) reaction wheel type actuators that are commonly utilized in the attitude control subsystem (ACS) of a satellite and 2) actuators in a two-link flexible joint manipulator.

Description: This paper develops techniques to design plug-in hybrid electric vehicle (PHEV) power management algorithms that optimally balance lithium-ion battery pack health and energy consumption cost. As such, this research is the first to utilize electrochemical battery models to optimize the power management in PHEVs. Daily trip length distributions are integrated into the problem using Markov chains with absorbing states. We capture battery aging by integrating two example degradation models: solid–electrolyte interphase (SEI) film formation and the “Ah-processed” model. This enables us to optimally tradeoff energy cost versus battery-health. We analyze this tradeoff to explore how optimal control strategies and physical battery system properties are related. Specifically, we find that the slope and convexity properties of the health degradation model profoundly impact the optimal charge depletion strategy. For example, solutions that balance energy cost and SEI layer growth aggressively deplete battery charge at high states-of-charge (SoCs), then blend engine and battery power at lower SoCs.

Description: In this article a novel piezo-hydraulic actuator exploiting resonance effects is presented. The operation of the proposed resonant fluid actuator (RFA) relies on the significant pressure raised in a fluid pipe during wave resonance. The piston of the actuator houses a valve, which rectifies the wave's motion into direct mechanical motion. A state-space model, derived from the compressible Navier–Stokes equations, is formed by linearizing these equations and then discretizing them using the Chebyshev collocation method. In order to track the deformable control space, the resulting equations are reformed in an Arbitrary Lagrangian Eulerian framework. The fluid model is augmented with the valve and piezo dynamics, and, therefore, the final model is capable of capturing the performance of the RFA. The overall model is formulated in a time-variant state-space form and an optimal controller is designed to maximize the differential pressure across the piston. Experimental studies are used to investigate the efficacy of the proposed modeling approach, via utilization of a wavelet transform and finite element analysis. Simulation studies and experimental data are offered for verification of the actuator's principle of operation.

Description: This paper presents a vision-based collision avoidance technique for small and miniature air vehicles (MAVs) using local-level frame mapping and planning in spherical coordinates. To explicitly address the obstacle initialization problem, the maps are parameterized using the inverse time-to-collision (TTC). Using bearing-only measurements, an extended Kalman filter is employed to estimate the inverse TTC, azimuth, and elevation to obstacles. Nonlinear observability analysis is used to derive conditions for the observability of the system. Based on these conditions, we design a path planning algorithm that simultaneously minimizes the uncertainties in state estimation while avoiding collisions with obstacles. The behavior of the planning algorithm is analyzed, and the characteristics of the environment in which the planning algorithm is guaranteed to generate collision-free paths for MAVs are described. Numerical results show that the proposed method is successful in solving the path planning problem for MAVs.

Description: In this brief, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation techniques. The fault diagnosis method follows a general architecture developed in previous papers, where a fault detection estimator is used for fault detection, and a bank of nonlinear adaptive fault isolation estimators are employed to determine the particular fault type/location. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. The general FDI architecture is applied to a realistic nonlinear aircraft engine model recently developed by NASA Researchers. In addition, the robustness of the fault diagnostic method with respect to normal engine health degradation is enhanced by adaptive thresholds and adaptive approximation techniques, hence accurate diagnostic performance is maintained while the engine continues to degrade over its lifetime. Some representative simulation results are given to show the effectiveness of the robust nonlinear FDI method.

Description: In this brief, we discuss the design of electrostatic potential profile to achieve a desired electron transmission coefficient versus bias voltage characteristics in nanoscale semiconductor devices. This is a common problem in the design of many new electronic devices. We formulate it as a constrained optimization problem, and solve it by sequential linear programming. We further investigate the robust design of potential that is tolerant to noise, disturbance, and parameter uncertainty in the device.

Description: Energy consumption of a vehicle is greatly influenced by its driving behavior in highly interacting urban traffic. Strategies for fuel efficient driving have been studied and experimented with in various conceptual frameworks. This paper presents a novel control system to drive a vehicle efficiently on roads containing varying traffic and signals at intersections for improved fuel economy. The system measures the relevant information of the current road and traffic, predicts the future states of the preceding vehicle, and computes the optimal vehicle control input using model predictive control (MPC). A typical control objective is chosen to maximize fuel economy by regulating a safe head-distance or cruising at the optimal velocity under bounded driving torque condition. The proposed vehicle control system is evaluated in urban traffic containing thousands of diverse vehicles using the microscopic traffic simulator AIMSUN. Simulation results show that the vehicles controlled by the proposed MPC method significantly improve their fuel economy.

Description: This paper investigates the decentralized control for a large-scale system with an IP-based communication network. The decentralized controller design specifically takes the nonuniform communication-delay-distribution characteristic of the IP-based communication network into account. First, a networked decentralized control modeling for the large-scale system is proposed. The two main points are: 1) a decentralized controller is designed, which does not depend on the full-order state of the system and 2) the nonuniform communication-delay-distribution characteristic of the IP-based communication network is fully considered in the controller design. Second, if the probability distribution of communication delay is known a priori in the design process, sufficient stability and stabilization conditions for the networked large-scale system are derived in terms of linear matrix inequalities. It shows that, the solvability of the design depends not only on the probability distribution of the communication delay but also on the network topology. Finally, the design method is applied to two pendulums coupled by a spring and a quadruple-tank process. Simulation results demonstrate the effectiveness of the proposed method.

Description: The wafer stage and reticle stage in a lithographic tool, used to manufacture integrated circuits (ICs), operate at nanometer accuracy during a scanning motion. The position accuracy and settling time after accelerating to the scanning velocity are largely determined by the feedforward controller. The feedforward controller calculates the required force for the stage to move according to its position profile by multiplying the reference acceleration with the known stage mass. Two effects limit the accuracy directly after the acceleration phase. First, the actual stage acceleration in response to the controller-calculated force depends on the position of the stage in its working range. A variation of 0.1% is observed. Second, dynamic resonances in the stage response require a higher-order feedforward model. Combined, these effects result in a 20–30 nm peak position error. This brief investigates online adaptation of the feedforward mass parameter, with the aim of reducing the position-dependent stage behavior. Adaptation of only the feedforward mass is shown not to be able to compensate for stage dynamics, additionally requiring a higher-order feedforward element. From the control force and the measured motion response, the feedforward mass parameter is estimated on line. Least-squares estimation is fast enough to update the mass parameter during the acceleration phase, which takes less than 100 ms. This brief addresses a number of practical aspects and shows that adaptive feedforward estimation in combination with higher-order feedforward reduces the peak position error consistently by creating position-independent behavior.

Description: To solve nonrepetitive problems, this brief proposes and investigates a novel repetitive motion planning (RMP) scheme (termed acceleration-level RMP scheme), which is resolved at the joint-acceleration level rather than at the joint-velocity level. The scheme is then reformulated as a quadratic program (QP) subject to equality and bound constraints. For the purposes of experimentation, a discrete-time QP solver is developed for the solution of the resultant QP. In addition, the global convergence of such a discrete-time QP solver is presented and analyzed. Comparisons between the nonrepetitive motion and repetitive motion planning validate the effectiveness and superiority of the proposed scheme. More importantly, the proposed scheme and the corresponding discrete-time QP solver are implemented on a six-link planar robot manipulator. The experimental results further substantiate their physical realizability, efficiency, and accuracy.

Description: Inertia friction welding (IFW) is a rotational pressure welding process and is a technique commonly used in the production of aircraft engine shafts. With the ever-increasing achievable production tolerances in terms of the upset and angular orientation, it follows that a significant improvement in the control of these quality parameters is called for. The approaches used for this purpose up to now were based on conventional control algorithms which considered only partial feedback and failed to comply with the required narrow tolerances. The objective of this brief is to develop optimal control algorithms which use feedback from the upset and angular orientation and are able to reach the necessary accuracy. The efficacy of these algorithms is established through experimental data collected from an actual IFW test bench.

Description: The real-time balance control of an eight link biped robot using a zero moment point (ZMP) dynamic model is difficult due to the processing time of the corresponding equations. To overcome this limitation, an intelligent computing control technique is used. This technique is based on support vector regression (SVR). The method uses the ZMP error and its variation as inputs, and the output is the correction of the robot's torso necessary for its sagittal balance. The SVR is trained based on simulation data and their performance is verified with a real biped robot. The ZMP is calculated by reading four force sensors placed under each robot's foot. The gait implemented in this biped is similar to a human gait that is acquired and adapted to the robot's size. Some experiments are presented, and the results show that the implemented gait combined with the SVR controller can be used to control this biped robot. The SVR controller performs the control in 0.2 ms.

Description: Control of exhaust gas recirculation (EGR) and variable geometry turbine in diesel engines is a challenging problem and model predictive control (MPC) seems to be a promising method. In MPC the choice of output variables, and thereby the criterion, has a direct impact on the optimization problem to solve and the resulting control performance. Different selections of outputs are investigated and discussed, proposing that it is beneficial to include EGR-fraction and pumping losses in the criterion while having the oxygen/fuel ratio as a constraint. The rational for this constraint is that, in diesel engines, it is allowed to have the oxygen/fuel ratio larger than a set-point. The proposed design also includes integral action of the EGR-fraction to handle model errors and prediction of engine load and speed. A comparison is made between the proposed MPC, a proportional–integral–derivative (PID) controller, and an MPC with intake manifold pressure and compressor flow as outputs, which is the common choice in the literature. Comparisons are performed in simulation on the European transient cycle showing the following two points. First, the proposed design gives 9% lower oxygen/fuel ratio error, 80% lower EGR-error, and 12% lower pumping losses compared to an MPC design with intake manifold pressure and compressor flow as outputs. Second, the proposed design gives 9% lower EGR-error and 6% lower pumping losses compared to a control structure with PID controllers with oxygen/fuel ratio and EGR-fraction as the main outputs.

Description: This brief describes a control strategy for the throwing motion of an underactuated two-link planar robot called the Pendubot. The springed Pendubot is built based on the concept of unstable zero dynamics, and our investigation uses it as a dynamic model of superior limbs to imitate human throwing motion. In the proposed control strategy, the zero dynamics is intentionally destabilized when a ball held by the end-effector is constrained on a geometric path in a vertical plane, using output zeroing control for the deviation between the ball and geometric path. The unstable zero dynamics drives the ball along the geometric path to achieve fast and accurate throw in a desired direction. The unstable zero dynamics is analytically derived to guarantee the dynamic acceleration of the ball along the geometric path. Numerical simulations and experimental results confirm the effectiveness of the proposed control strategy.

Description: Time delay in bilateral control system seriously deteriorates performance and stability. Acceleration-based bilateral control (ABC) is hybrid of position and force control in the acceleration dimension using the disturbance observer. It can be divided into two modal spaces: common and differential. The sum of master and slave forces is controlled to be zero in the common modal space $(1,+{1})$ to realize the law of action–reaction. The difference of master and slave positions is controlled to be zero in the differential modal space $(1,-{1})$ for position tracking. This brief analyzes the stability of each modal space under time delay. Based on modal space analysis, this brief proposes a novel four-channel (4ch) ABC architecture using two degrees of freedom proportional derivative (PD) control for haptic communication under time delay. In the proposed 4ch ABC, the difference of position is controlled to be zero by P–D control (differential proactive PD control), and the sum of the forces is controlled to be zero by damping-injected force P control. Furthermore, this brief utilizes frequency-domain damping design to realize both high performance and stability based on delay-dependent robust $H_{infty}$ stability. The proposed 4ch ABC improves the stability of each modal space under time delay. The validity of the proposed control system is confirmed by some experimental results.

Description: This brief presents a practical solution to the problem of monitoring an environmental process in a large region by a small number of robotic sensors. Optimal sampling strategies are developed, taking into account the quality of the estimated environmental field and the lifetime of the sensors. We also present experimental results for monitoring a temperature field of an outdoor swimming pool sampled by an autonomous aquatic surface robot. Simulation and experimental results are provided to validate the proposed scheme.

Description: For the forced-circulation evaporation process of alumina production, the control objectives include maintaining the liquid level and fast tracking of the product density with respect to its setpoint. However, with the strong coupling between the level control and product density control loops and the process exhibits strong nonlinearities, conventional control strategies, such as proportional-integral-derivative and other linear control design, cannot achieve satisfactory control performance and meet production demands. By augmenting the forced-circulation evaporation system model with dynamics of its operating valves, a nonlinear adaptive decoupling switching control strategy is proposed. This control strategy includes a linear adaptive decoupling controller, a neural-network-based nonlinear adaptive decoupling controller, and a switching mechanism. The linear adaptive decoupling controller is used to reduce the coupling between the two control loops. The neural-network-based nonlinear adaptive decoupling controller is employed to improve the transient performance and reduce the effects of the nonlinearities on the system, and the switching mechanism is introduced to guarantee the input–output stability of the closed-loop system. Simulation results show that the proposed method can decouple the loops effectively for the forced-circulation evaporation system and thus improve the evaporation efficiency.

Description: This brief studies the stability of single-machine infinite-bus power systems with superconducting magnetic energy storage (SMES) via simultaneous design of generator excitation control and SMES control. Since the object model is of non-strict-feedback form and thus the traditional backstepping method is not applicable, we propose an extended backstepping method for a class of general nonlinear systems. Then, we apply the proposed method to the object model and design the generator excitation controller and SMES controller simultaneously, which stabilize the power angle, the generator terminal voltage, and the power oscillation. In addition, we improve the transient and steady-state performances of power systems by introducing class $K$ functions in the design process. Simulation results also show the effectiveness and advantage of the proposed controller.