ALBERT

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Description: In the context of hybrid anti-lock brake systems, a closed-loop wheel-acceleration controller based on the observation of the extended braking stiffness (XBS) is provided. Its objective is to improve the system's robustness with respect to changes in the environment (as changes in road conditions, brake properties, etc.). The observer design is based on Burckhardt's tire model, which provides a wheel acceleration dynamics that is linear up to time-scaling. The XBS is one of the state variables of this model. This paper's main result is an observer that estimates this unmeasured variable. When the road conditions are known, a 3-D observer solves the problem. However, for unknown road conditions, a more complex 4-D observer must be used instead. In both the cases, the observer's convergence is analyzed using tools for switched linear systems that ensure uniform exponential stability (provided that a dwell-time condition is satisfied). Both experiments and simulations confirm the convergence properties predicted by the theoretical analysis.

Description: This brief proposes an optimal preview control law for a dual-stage actuator (DSA) system to track triangular reference, which is essential in many raster scan motion control applications. The main difficulty of tracking triangular reference is to follow the waveform fast and accurately when its slope switches. For this goal, we first discuss a time-optimal controller (TOC) for the primary stage, which is a double integrator system. The TOC is then proved to achieve the minimum overshoot. This result is a new contribution to DSA control applications because it leads to a reference profile of minimum amplitude for the secondary stage to follow and thus significantly prevents the saturation of the secondary actuator. Subsequently, we propose an optimal preview control with the use of the information of the future reference to further reduce the settling time and overshoot. Finally, the secondary actuator controller is also given. Experimental results are shown to verify the effectiveness of the proposed control design.

Description: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.

Description: In this paper, we address the tracking control problem for a unicycle-type mobile robot which is remotely controlled by a two-channel, delay-inducing communication network. A predictor-based control strategy capable of controlling the negative effects of the time-delay is proposed. Moreover, conditions are provided guaranteeing the local or global asymptotic stability of the closed-loop system up to a maximum admissible delay. The applicability of the proposed predictor-controller combination is demonstrated using an interconnected robotic platform located partly in Eindhoven, the Netherlands, and Tokyo, Japan.

Description: Reducing the cantilever quality $(Q)$ factor in the atomic force microscope (AFM), when operating in tapping mode, allows for an increase in imaging speed. Passive piezoelectric shunt control has several advantages over alternative methods of cantilever $Q$ factor reduction. However, this technique uses a passive electrical impedance to modify the mechanical dynamics of the cantilever, which limits the amount of $Q$ factor reduction achievable. This paper demonstrates that further reductions in the cantilever $Q$ factor may be obtained with the use of an active impedance in the piezoelectric shunt control framework. The active impedance parameters are designed in such a way that the piezoelectric shunt controller emulates a positive position feedback controller in a displacement feedback control loop. A significant reduction in cantilever $Q$ factor is obtained using an active impedance compared with that achieved with a passive impedance. The improvement in scan speed using this control technique is demonstrated with AFM images of a test sample.

Description: Available acoustical signal processing algorithms for active noise control (ANC) can only attenuate noise at a finite number of points. Accordingly, they are unable to create continuous quiet zones in 3-D space. This paper proposes a methodology for developing a family of acoustical signal processing algorithms that are able to control sound in three dimensions. The theoretical analysis is carried out for spherical quiet zones; however, the proposed methodology can apply to quiet zones with arbitrary shapes. It is assumed that there is no reflection while sound propagates in media. An arbitrary frequency spectrum for the noise to be canceled is considered. An optimal control system for 3-D ANC is then derived. Also, an efficient method for the implementation of the proposed system is developed. The implementation cost of the proposed system is comparable with that of conventional ANC systems. In addition to the simulation results, different experiments with an experimental ANC system show the validity of the theoretical results.

Description: We propose a control mechanism to obtain opportunistic high resolution facial imagery, via distributed constrained optimization of the pan, tilt, zoom (PTZ) parameters for each camera in a sensor network. The objective function of the optimization problem quantifies the per camera per target image quality. The tracking constraints, which are a lower bound on the information about the estimated position for each target, define the feasible PTZ parameter space. Each camera alters its own PTZ settings. All cameras use information broadcast by neighboring cameras such that the PTZ parameters of all cameras are simultaneously optimized relative to the global objective. At certain times of opportunity, due to the configuration of the targets relative to the cameras, and the fact that each camera may track many targets, the camera network may be able to reconfigure itself to achieve the tracking specification for all targets with remaining degrees of freedom that can be used to obtain high-res facial images from desirable aspect angles for certain targets. The challenge is to define algorithms to automatically find these time instants, the appropriate imaging camera, and the appropriate parameter settings for all cameras to capitalize on these opportunities. The solution proposed herein involves a Bayesian formulation in a game theoretic setting. The Bayesian formulation automatically trades off objective maximization versus the risk of losing track of any target. This paper describes the problem and solution formulations, design of aligned local and global objective functions and the inequality constraint set, and development of a distributed Lagrangian consensus algorithm that allows cameras to exchange information and asymptotically converge on a pair of primal–dual optimal solutions. This paper presents the theoretical solution along with the simulation results.

Description: This paper presents the modeling, control system design, and experimental results for a prototype lighter-than-air wind energy system being pioneered by Altaeros Energies. This unique design features a horizontal-axis turbine that is elevated to high altitudes through a buoyant shroud, which is tethered to a ground-based platform. The system's altitude can be adjusted to maximize power production, and because the system is both functional and economical in a stationary position, it circumvents many of the controls challenges faced by kite-based wind energy systems. However, the need for generation of energy introduces pointing, efficiency, and autonomy requirements, which are not faced by conventional aerostats, thereby requiring a careful model-based control design. In this paper, we provide a dynamic model of the Altaeros system, then show how this model is leveraged in the plant design and in the design of the control system, which provides full autonomy, from takeoff, through power production, to autonomous landing. We provide simulation and experimental results that demonstrate the performance of the prototype and point to important areas where Altaeros will focus its efforts moving forward.

Description: In this paper, we design, analyze, and experimentally validate an observer-based backstepping controller for electropneumatic actuators. These actuators are commonly used in diesel engines for variable geometry turbochargers (VGT) control. They are preferred over other actuation techniques due to better force/weight ratio; however, their drawbacks, such as nonlinear air mass flow, friction, and aerodynamic forces make their control as a challenging problem. The aim of this paper is to achieve precise control of the actuator by compensating friction and aerodynamic VGT forces. A Lyapunov-based output feedback controller is developed using backstepping technique. As the actuator position is the only measured state, pressure and friction states are obtained from sliding mode-based observers. The exponential convergence of the controller–observer closed-loop system is proved using Lyapunov analysis. Experimental results are presented, which demonstrate the effectiveness of the proposed controller.

Description: This paper presents experimental results concerning the control of a distributed solar collector field, where the main objective concerns the regulation of the outlet oil temperature by suitably manipulating the oil flow rate. This is achieved by means of a constrained nonlinear adaptive model-based predictive control framework where the control action sequence is obtained by solving an open-loop optimization problem, subject to a set of constraints. The plant dynamics is approximated by an affine state-space neural network, whose complexity is specified in terms of the cardinality of dominant singular values associated with a subspace oblique projection of data-driven Hankel matrices. The neural network is first trained offline and subsequently improved through a recursive updating of its weights and biases, based on a dual unscented Kalman filter. The control scheme is implemented on the Acurex field of the Plataforma Solar de Almería, Spain. Results from these experiments demonstrate the feasibility of the proposed framework, and highlight the ability to cope with time-varying and unmodeled dynamics, under the form of disturbances, and its inherent capability for accommodating actuation faults.

Description: In this brief, the transient performance of the signal transformation approach (STA) is considerably enhanced by initializing the state vector of the compensator to appropriate values. For triangular reference tracking, it is shown that the proposed method is identical to the impulsive state multiplication (ISM) approach. Through simulations and experiments, we also show that the proposed method can be equally applied to improve the STA for arbitrarily shaped desired signals, where ISM is not applicable. Tracking efficacy of the proposed method compared with that of an ordinary feedback loop with a similar noise rejection performance is also demonstrated.

Description: In a yacht cabin, high-quality absorbing materials can reduce in a good way medium and high-frequency noises, but they are completely inefficient in the case of low-frequency noises. In this brief, a detailed analysis of low-frequency noises in a yacht environment is presented; several boat conditions, depending on the presence of different noise sources (e.g., air conditioning, engines) and various navigation velocities are investigated. In this context, a specific multichannel feedback active noise control system, derived from the well-known filtered-x approach, is designed and installed in a bedroom of a yacht in order to reduce the acoustical annoyance using four error microphones and two sub-woofers for antinoise generation. The results of practical experiments are shown in order to demonstrate the effectiveness of the proposed system.

Description: In this brief, an indirect adaptive control methodology for attenuation of multiple unknown time varying narrow-band disturbances is proposed. This method is based on the real time estimation of the frequency of narrow-band disturbances using adaptive notch filters followed by the design of a controller using adjustable band-stop filters for the appropriate shaping of the output sensitivity function. A Youla-Kučera parametrization of the controller is used for reducing the computation load. This approach is compared on an active vibration control system with the direct adaptive control scheme based on the internal model principle proposed. Real time experimental results are provided.

Description: This paper addresses the formation control problem for fleets of autonomous underwater vehicles (AUVs). The solution is based on the virtual leader approach, with the main goal of designing a control system to cope with the inter-vehicle communication problems, especially significant in underwater environments. The use of kinematic relations allows the linearization of the AUV dynamics maintaining its turning capacities. The control strategy consists of a feedback $H_{2}/H_{infty}$ controller in combination with a feedforward controller, which makes it possible to deal with delays and packets dropouts while ensuring a good formation control performance.

Description: This brief proposes a method for designing a disturbance observer (DOB) to decouple joint interactions in robot dynamics with nonlinearity. The traditional DOB based on filter design theory has limited performance since the cut-off frequency of its $Q$ -filter is the only tunable parameter to deal with disturbance suppression and model uncertainty. In this brief, a robust optimal design method is developed for the DOB, which can achieve optimal performance of suppressing disturbance by systematically shaping its $Q$ -filter. Simulation results of application to a two-link manipulator with flexible joints show the improvements in disturbance suppression, which illustrates the validity of the proposed method.

Description: In this letter, we indicate some fallacies of the main results in the comment paper. Some inconsistencies and incompleteness concerning on the expressions of control laws, the constraints of initial states, and the selections of control parameters are pointed out and corrected. New control laws are proposed to avoid the fallacies in this paper.

Description: This brief addresses a networked control system problem: scheduling the transmission times in a time division multiple access (TDMA) architecture with a set of sensors embedded in a distributed control application. With TDMA, collisions are avoided, delays are bounded, and with the appropriate schedule, the data rate can be reduced to a minimum required stability. To further reduce the transmitted information, a controller-side observer is implemented. Two approaches are then proposed: 1) periodic transmission schedule, for which a periodic linear system analysis is done and 2) aperiodic schedule, where optimality criteria for schedule design are found. In both cases, little feedback is required from the controllers to the sensors, just an initial configuration.

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Description: Analysis and minimization of interaction in multivariable systems employing decentralized controllers with application to roll-to-roll manufacturing systems are considered in this paper. A new interaction metric based on the Perron–Frobenius theory of nonnegative matrices is presented. This new metric may be used to quantify interaction in a large-scale interconnected system, establish constraints on closed-loop system stability, and provide a systematic design procedure for constructing decentralized pre-filters, which minimize interaction. The new interaction metric is applied to a roll-to-roll (R2R) manufacturing system, which utilizes decentralized control systems. R2R manufacturing is a continuous process in which flexible materials are transported on rollers through processing machinery where operations, such as printing, coating, lamination, etc., are performed to obtain finished products. Based on the Perron root interaction metric (PRIM), a comprehensive experimental study to analyze and minimize interaction on a large experimental R2R platform is presented. A representative sample of experimental results which demonstrate the applicability of PRIM is presented and discussed.

Description: Technological advances made wireless sensors cheap and reliable enough to be brought into industrial use. A major challenge arises from the fact that wireless channels introduce random packet dropouts. Power control and coding are key enabling technologies in wireless communications to ensure efficient communication. In this paper, we examine the role of power control and coding for Kalman filtering over wireless correlated channels. Two estimation architectures are considered; initially, the sensors send their measurements directly to a single gateway (GW). Next, wireless relay nodes provide additional links. The GW decides on the coding scheme and the transmitter power levels of the wireless nodes. The decision process is carried out online and adapts to varying channel conditions to improve the tradeoff between state estimation accuracy and energy expenditure. In combination with predictive power control, we investigate the use of multiple-description coding (MDC), zero-error coding (ZEC), and network coding and provide sufficient conditions for the expectation of the estimation error covariance matrix to be bounded. Numerical results suggest that the proposed method may lead to energy savings of around 50%, when compared with an alternative scheme, wherein transmission power levels and bit-rates are governed by simple logic. In particular, ZEC is preferable at time instances with high channel gains, whereas MDC is superior for time instances with low gains. When channels between the sensors and the GW are in deep fades, network coding improves estimation accuracy significantly without sacrificing energy efficiency.

Description: In this paper, a phenomenological model of pneumatic muscle is established consisting of a contractile element, spring element, and damping element in parallel. To verify the practicability of pneumatic muscle (PM) modeling, dynamic surface control (DSC) characterized by convenient design and good transient performance is employed for realizing PM tracking control. However, parametric uncertainty is inevitable in PM modeling as friction and unknown external disturbances exist in a PM system. These PM modeling errors and unknown variables can undermine and deteriorate the control performance of PM systems. To solve this problem and improve control accuracy, a novel nonlinear disturbance observer-based dynamic surface control (NDOBDSC) is proposed for trajectory tracking of PM system. Through employing the nonlinear disturbance observer, the stated uncertainties can be estimated online and compensated. The proposed novel control scheme therefore integrates the advantages of DSC, while estimating time-varying uncertainties to achieve compensation of inherent uncertainties. The established control law guarantees that the closed-loop system is semiglobally uniformly and ultimately bounded. Both the simulation studies and practical experiments demonstrate the effectiveness of NDOBDSC, showing that the control performance of NDOBDSC is satisfactory in the presence of modeling errors, friction, changing load, and other uncertainties in the PM system.

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Description: This paper is concerned with time-domain optimal control of active suspensions. The optimal control problem formulation is generalized by incorporating both road disturbances (ride quality) and a representation of driver inputs (handling quality) into the optimal control formulation. A regular optimal control problem as well as a risk-sensitive exponential optimal control performance index is considered. Emphasis is placed on practical considerations including the issue of state estimation in the presence of load disturbances (driver inputs).

Description: Hydrodynamic dynamometers are employed in internal combustion engine (ICE) test benches for the entire range of ICEs, from cart engines to large ship engines. They are inexpensive and characterized by a relatively small moment of inertia. Although nonlinearities and the absence of accurate models make controlling these dynamometers a difficult task, they are currently used almost exclusively for stationary measurements. To overcome this limitation, this paper proposes an inverse torque control based on a Wiener approximation whose parameterization is strongly simplified by choosing a suitable set of basis functions for the nonlinear map. As inverting this nonlinear map would be computationally demanding, it is replaced with a dynamic inversion which—together with the actuator redundancy—allows additional optimality criteria to be incorporated. The performance of the proposed control law is validated by measurements on a test bench and shows significant improvement compared with classical implementations.

Description: In this paper, we present two decentralized algorithms that aim to achieve uniform lighting across the floor of an experimental testbed under a variety of challenges, including cross-illumination effects and external light disturbances. These challenges cause over-illuminations in the environment that result in a waste of energy and discomfort to the occupants. First, a decentralized integral control approach that does not have any communication between the lights is developed and applied to the system. Due to its failure in achieving uniform lighting when the cross-illumination effects are maximized, a new decentralized method called the illumination balancing algorithm (IBA) is developed that takes the local light levels into account when adjusting the light voltages. The stability analysis of the IBA for the full height partitions case of the testbed is shown as well as the regulation problem results where the algorithm successfully balances the illuminations and hence achieves uniform lighting. In order to track a desired light level across the zones, the IBA is augmented with an integral control at an arbitrarily selected control loop. This combined algorithm achieved successful control even in a case where the decentralized integral control failed.

Description: In this paper, control system designs are proposed for the Broken River in Victoria, Australia. The aim of the control system is to improve water resource management and operation for the benefit of irrigators and the environment. Both centralized and decentralized control schemes are considered. The decentralized scheme consists of a number of PI and I controllers, while the centralized scheme is a model predictive controller. The controllers are designed based on simple models obtained using system identification methods. In a realistic simulation scenario, the control systems compared very favorably with current manual operation offering increased operational flexibility with a significant potential for substantial water savings, improved level of service to irrigators, and improved environmental benefits.

Description: This paper proposes a practical and effective disturbance attenuation control algorithm to support the safe and comfortable operation of power-assist wheelchairs. The power-assist wheelchair is an option for mobility that can be propelled by the user's arm while also providing additional assisting torque based on the measured user torque to relieve the user's propulsion effort. The control algorithm proposed in this paper detects external forces other than the applied human torque and attenuates the effect of the external force to improve safety and ease of use. The proposed 2-D disturbance attenuation control can reduce the effect of external force in the longitudinal direction and the rotational direction respectively. Gravity on slopes is considered the major disturbance, and the effectiveness of the proposed controller on slopes is verified by experiments.

Description: In this paper, a stochastic optimal control scheme for the air–fuel ratio is proposed, which considers the cyclic variations of the residual gas fraction (RGF). Initially, a cylinder pressure-based measurement of the RGF is derived by following the physics of inlet–exhaust process. Then, a dynamical model is presented to describe the cyclic variation of the air charge, fuel charge, and combustion products under a cyclically varied RGF, where the RGF is modeled as a Markovian stochastic process. Using this model, a feedback control law is derived, which optimizes the quadratic cost function in the stochastic sense with respect to the stochastic property of the residual gas. The cost function reflects the tradeoff between the accuracy of the regulation of the air–fuel ratio with the fluctuation in the fuel injection. Finally, a sampling process-based statistical analysis for the RGF is presented based on the experiments conducted on a full-scaled gasoline engine test bench, and the proposed control law is validated based on a numerical simulation and experiments.

Description: The following paper was selected for the 2013 IEEE Transactions on Control Systems Technology Outstanding Paper Award from papers that appeared in 2011–2012:

Description: Iterative learning control can be applied to systems that execute the same tracking task over a finite time duration. An execution is known as a trial, and once each is complete, the system resets to the starting location and the next trial begins. All previous trial information is available for use in constructing the control input for the next trial, and the basic idea is to improve tracking performance from trial-to-trial. This brief analyzes the effects of nonminimum phase zeros on the trial-to-trial error norm convergence of norm optimal iterative learning control, a commonly used algorithm, for differential linear systems with supporting experimental results from a test facility.

Description: Magnetic shape memory (MSM) actuators belong to active material technologies with a high energy density and outstanding field-strain relation. Large-scale multivariate field-stress-strain hysteresis effects, however, antagonize their broad use because of the inherent difficulties with control. In this brief, we describe and compare experimentally several MSM control strategies, including the observer-based inverse hysteresis approach proposed in the previous works and combined here with a linear feedback controller by connecting both in parallel. For a prototypic MSM actuator with return spring, it is shown that the actuator plant can be approximated by an appropriate hysteresis operator and a linear transfer function of residual dynamics. The positioning profiles with a bandwidth 0.1–10 Hz and amplitudes between 0.01 and 0.1 mm have been evaluated by the experiments.

Description: This brief describes an active control method to prevent unwanted nonlinear vibration response modes of a rotor-dynamic system. Nonlinear stiffness of components that support or surround a machine rotor can cause a response branch that extends critical vibration (resonance) over a wide interval of rotational speeds. Within this interval, jump transitions between alternative low amplitude and high amplitude response modes become possible. This brief explains how such behavior can be eliminated by applying control forces to the rotor based on dynamic feedback of strains measured in the stator structure. An optimal model-based controller synthesis is considered that combines a Lur'e-type Lyapunov function with a quadratic cost measure to penalize controller gain and bandwidth. Results are presented for an experimental flexible rotor system where nonlinear rotor–stator interaction occurs through a bearing with radial clearance. An active magnetic bearing applies control forces to the rotor in a separate plane. The results show that the control technique can eliminate jump response modes and can significantly reduce mechanical stress associated with rub interaction of the rotor and stator. The influence of key parameters in the model and controller formulation is shown.

Description: We solve the control problem of switched-reluctance motors without velocity measurements. Our controller is composed of a loop in the mechanical dynamics which consists of a ${rm PI}^{2}{rm D}$ controller and a tracking controller closing an inner loop with the stator currents dynamics. The ${rm PI}^{2}{rm D}$ controller consists of a linear proportional derivative controller in which the measurement of velocities is replaced by approximate derivatives of angular position. Then a double integrator is added, composed of an integral of the angular position errors and a second integral correction term in function of the approximate derivative. We show global exponential stability and illustrate the performance of our controller in numerical simulations.

Description: This paper is devoted to the finite-time synchronous control problem for multiple manipulators subject to sensor saturations. An effective framework through defining a class of coordinated saturation functions is introduced, under which the distributed protocols with continuous feedbacks are constructed. By applying the homogeneous theory for stability analysis, it is proven that all the multiple manipulators converge to the target position in finite time without disturbances. In the presence of disturbances, it is shown that the position errors can reach a neighborhood of the origin in finite time. Numerical simulations on four robot manipulators with two degrees of freedom are presented to demonstrate the efficiency of our proposed protocols.

Description: Model predictive control (MPC) is a popular strategy, often applied to distributed parameter systems (DPSs). Most DPSs are approximated by nonlinear large-scale models. Using it directly for control applications is problematic because of the high associated computational cost and the nonconvexity of the underlying optimization problem. In this brief, we build on the notion of multiple MPC, combining it with equation-free model reduction techniques, to identify the (relatively low-dimensional) subspace of slow modes and obtain a local reduced-order linear model. This procedure results in an input/output framework, enabling the use of black-box deterministic and stochastic simulators. The set of linear low-dimensional models obtained off-line along the reference trajectories are used for linear MPC, either with off-line gain scheduling or with online identification of the reduced model. In the former approach, the decision to use the model in real time is taken a priori , whereas in the latter a local model is computed online as a function of a set stored in a model bank. The two approaches are discussed and validated using case studies based on a tubular reactor, a highly nonlinear dissipative partial differential equation system exhibiting instabilities and multiplicity of state.

Description: This paper presents the development of an indirect intelligent sliding mode control (IISMC) system for use with antagonistic shape memory alloy (SMA) actuators. The controller manipulates input voltages to a pair of offset antagonistic SMA tendons, resulting in heat-induced bending of a flexible beam. Hysteresis compensation is achieved using a pair of hysteretic recurrent neural networks, which map the nonlinear, hysteretic relationships between SMA temperatures and bending angle for each tendon. The sliding mode control law regulates tendon temperatures based on reference and measured bending angle. Additionally, the IISMC incorporates an antislack component to increase tendon responsiveness. Experimental results demonstrate precise tracking of periodic reference trajectories. The controller is robust to model uncertainty, disturbances, and parameter variations, with bending angle tracking results superior to those of an ${rm optimized~proportional}+{rm integral~controller}$ .

Description: A new nonlinear controller design is developed for speed regulation of voltage source inverter (VSI)-driven induction motors. The proposed controller directly provides the duty-ratio input of the VSI in the permitted range to ensure linear modulation, is fully independent from the system parameters and suitably regulates the motor speed and the stator flux to the desired values. Considering the complete nonlinear model of the converter-motor system and applying advanced nonlinear methods, boundedness of the full system states is proven. Furthermore, exploiting the Hamiltonian-passive structure of the system, state convergence to the equilibrium is shown using LaSalle's Invariance Principle. Though the controller design is developed in the frame of the field-oriented control methodology, stability holds true even without accurate field orientation guaranteeing an effective performance in cases where parameter variations occur. Extensive simulation results on an industrial size system are conducted to evaluate the proposed controller performance, under rapid changes of the reference speed or load torque as well as system parameter variations. In addition, a lab size induction motor system is experimentally tested. In all cases, the system response shows fast convergence to the equilibriums after limited transients, thus verifying the theoretical results.

Description: This brief studies the active control of compressor surge using the thrust active magnetic bearing (AMB) in centrifugal compressors and it presents experimental test data to demonstrate the effectiveness of the control solution in industrial size equipment. A detailed step-by-step description is provided from the derivation of the surge model, to the implementation and experimental testing of the surge controller. To accurately represent the configurations found in industrial systems, the proposed surge suppression method is implemented integrally using common off-the-shelf components. During the design of the surge controller, special attention is paid to a possible conflict between the surge controller and the AMB's internal rotor levitation controller. Observations from the experimental surge testing demonstrate that the presented method is able to stabilize the compression system when operating in the unstable surge region, extending the stable flow range by over 21%. In practice, this allows the compressor to operate at the peak pressure/efficiency point, while maintaining a healthy surge margin as commonly prescribed in industrial compressors.

Description: In this brief paper, a Stochastic Model Predictive Control formulation tractable for large-scale systems is developed. The proposed formulation combines the use of Affine Disturbance Feedback, a formulation successfully applied in robust control, with a deterministic reformulation of chance constraints. A novel approximation of the resulting stochastic finite horizon optimal control problem targeted at building climate control is introduced to ensure computational tractability. This work provides a systematic approach toward finding a control formulation which is shown to be useful for the application domain of building climate control. The analysis follows two steps: 1) a small-scale example reflecting the basic behavior of a building, but being simple enough for providing insight into the behavior of the considered approaches, is used to choose a suitable formulation; and 2) the chosen formulation is then further analyzed on a large-scale example from the project OptiControl, where people from industry and other research institutions worked together to create building models for realistic controller comparison. The proposed Stochastic Model Predictive Control formulation is compared with a theoretical benchmark and shown to outperform current control practice for buildings.

Description: A sequential and multihypothesis probability ratio test is proposed for detecting and identifying a bias fault in GPS pseudorange measurements. Initially, a measurement residual variable that is only a function of the measurement noise and the possible bias fault is constructed. The probability of this residual given a certain bias hypothesis is then obtained. Subsequently, an error variable is constructed for each hypothesis based on the ratio of the probability of that hypothesis to the probability of a base hypothesis. The propagation of the error variables with time is monitored for all hypotheses. If a hypothesis is associated with the true bias on the satellite measurement, then the corresponding error variable will remain around zero in mean. Otherwise, in case of a wrong hypothesis, the associated error variable will diverge away from zero. Error bounds for declaring false hypotheses are formulated in this brief. The advantage of the proposed method is that false hypotheses are continuously removed from the hypothesis set when their error variables exceed the error bound. Therefore, the size of the hypothesis set will reduce with time, ending up with only the correct bias hypothesis. This will result in a monotonic reduction in the computational time of the method. Finally, an ultratightly coupled filter structure is used to test the performance of the proposed method and the obtained results will be presented.

Description: Alternative and more efficient computational methods can extend the applicability of model predictive control (MPC) to systems with tight real-time requirements. This paper presents a system-on-a-chip MPC system, implemented on a field-programmable gate array (FPGA), consisting of a sparse structure-exploiting primal dual interior point (PDIP) quadratic program (QP) solver for MPC reference tracking and a fast gradient QP solver for steady-state target calculation. A parallel reduced precision iterative solver is used to accelerate the solution of the set of linear equations forming the computational bottleneck of the PDIP algorithm. A numerical study of the effect of reducing the number of iterations highlights the effectiveness of the approach. The system is demonstrated with an FPGA-in-the-loop testbench controlling a nonlinear simulation of a large airliner. This paper considers many more manipulated inputs than any previous FPGA-based MPC implementation to date, yet the implementation comfortably fits into a midrange FPGA, and the controller compares well in terms of solution quality and latency to state-of-the-art QP solvers running on a standard PC.

Description: Skilled welders can estimate and control the weld joint penetration, which is primarily measured by the backside bead width, based on weld pool observation. This suggests that an advanced control system could be developed to control the weld joint penetration by emulating the estimation and decisionmaking process of the human welder. In this paper an innovative 3-D vision sensing system is used to measure the characteristic parameters of the weld pool in real-time in gas tungsten arc welding. The measured characteristic parameters are used to estimate the backside bead width, using an adaptive neuro-fuzzy inference system (ANFIS) as an emulation of skilled welder. Dynamic experiments are conducted to establish the model that relates the backside bead width to the welding current and speed. The dynamic linear model is first constructed and the modeling result is analyzed. The linear model is then improved by incorporating a nonlinear operating point modeled by an ANFIS. Because the weld pool needs to gradually change, being controlled by a skilled welder, a model predictive control is used to follow a trajectory to reach the desired backside bead width and the control increment is penalized. Because the weld pool is not supposed to change in an extremely large range, the resultant model predictive control is actually linear and an analytical solution is derived. Welding experiments confirm that the developed control system is effective in achieving the desired weld joint penetration under various disturbances and initial conditions.

Description: This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.

Description: In the conventional adaptive neural network control of robotic manipulator, the desired position of robot end effector is specified as a point or trajectory. In addition, it is usually difficult to guarantee the transient performance of adaptive neural network control system due to the initialization error of the weight of neural network. In this paper, a new control formulation is proposed for the adaptive neural network control of robotic manipulator, which unifies existing neural network control tasks such as setpoint control, trajectory tracking control, and trajectory tracking control with prescribed performance bound. The proposed method also includes a new adaptive neural network control scheme where the objective for the robot end effector can be specified as a dynamic region, instead of the desired position or trajectory. The stability of the closed-loop system is analyzed using Lyapunov-like analysis. Experimental results are presented to illustrate the performance of the proposed approach and the energy-saving property of the proposed neural network controller with dynamic region.

Description: A technique is presented for estimating the number of iterations needed for convergence of iterative adaptive ON-OFF controllers to optimal switching times, for certain controllers proposed for managing stepping motion in autonomous microrobots. An upper bound on output error as a function of error in ON-OFF switching times is obtained, and lower bounds on the change in switching times from iteration-to-iteration are used to estimate output error evolution. The simulation and experimental results from the test case of a piezoelectric microrobotic leg joint indicate reasonable agreement between estimated and actual error. The use of convergence estimates to improve controller design with respect to total energy consumption is then described.

Description: Currently, there is significant research into the inclusion of smart devices in wind turbine rotor blades, with the aim, in conjunction with collective and individual pitch control, of improving the aerodynamic performance of the rotors. The main objective is to reduce fatigue loads, although mitigating the effects of extreme loads is also of interest. The aerodynamic loads on a wind turbine blade have periodic and nonperiodic components, and the nature of these strongly suggests the application of iterative learning control. This paper employs a simple computational fluid dynamics model to represent flow past an airfoil and uses this to undertake a detailed investigation into the level of control possible by, as in other areas, combining iterative learning control with classical control action with emphasis on how performance can be effectively measured.

Description: This paper focuses on the design of the control system of massively actuated deformable mirrors for astronomical adaptive optics applications. The proposed solution is based on a high-frequency completely decentralized feedback combined with a hybrid low-frequency term, which is the sum of feedforward–feedback contributions. An appropriate in the field system identification procedure and an automatic gain optimization eventually lead to a complete self-tuning controller. The improvements of system performances are evaluated through high-fidelity simulations.

Description: The estimation of the gas concentration (process state) associated with an emitting stationary or moving source using a sensing aerial vehicle (SAV) is considered. The dispersion from such a gas source into the ambient atmosphere is representative of accidental or deliberate release of chemicals, or release of gases from the biological systems. Estimation of the concentration field provides a superior ability for source localization, assessment of possible adverse impacts, and eventual containment. The abstract and finite-dimensional approximation framework present couples theoretical estimation and control with computational fluid dynamics methods. The gas dispersion (process) model is based on the 2-D advection-diffusion equation with variable eddy diffusivities and ambient winds. The state estimator is a modified Luenberger observer with a collocated filter gain that is parameterized by the position of the SAV. The process-state (concentration) estimator is based on a 2-D adaptive, multigrid, multistep finite-volume method. The grid is adapted with local refinement and coarsening during the process-state estimation, to improve accuracy and efficiency. The 2-D motion dynamics of the SAV is incorporated into the spatial process and the SAV's guidance is directly linked to the performance of the state estimator. The computational model and the state estimator are coupled in the sense that grid refinement is affected by the SAV repositioning, and the guidance laws of the SAV are affected by grid refinement. Extensive numerical simulations serve to demonstrate the effectiveness of the coupled approach.

Description: In this brief a distributed fault detection and isolation (FDI) methodology for a network of heterogeneous multiagent systems with different dynamics and order from one another is proposed. An FDI filter is designed such that the effects of disturbances and control inputs on the residual signals are minimized (for accomplishing the fault detection task) subject to the constraint that the transfer matrix function from the faults to the residuals is equal to a preassigned diagonal transfer matrix (for accomplishing the fault isolation task). Moreover, by utilizing the proposed methodology, isolation of simultaneous occurring faults can also be handled. Sufficient conditions for solvability of the problem are obtained in terms of linear matrix inequality (LMI) feasibility conditions. The extended LMI characterization is then used to reduce the conservativeness of the solution by eliminating the couplings between the Lyapunov matrices and the agents' matrices. Simulation results presented demonstrate the effectiveness and capabilities of our proposed design methodology.

Description: The cavity frequency locking problem, which arises in the field of quantum optics, involves matching the resonant frequency of an optical cavity to that of an incoming laser. Although this problem is investigated using linear control techniques for small perturbations, in this brief, we address the nonlinear control problem of frequency locking an optical cavity. Models of the measurement nonlinearities inherent in optical cavities are derived using a singular perturbation approach and instantaneous bounds are placed on the observation errors. A convex set within which the error between the laser frequency and the cavity resonant frequency lies is then determined at each time step by solving two high-order polynomial equations. Based on a reduced-order model of the cavity, a time-varying Kalman filter is designed and simulation results are presented to validate our controller on a full-model of the cavity in the nonlinear region of operation.

Description: Internal waves are important to oceanographers because, as they travel, they are capable of displacing mass, such as plankton and small fish. This paper considers a group of drogues estimating the physical parameters that determine the dynamics of an ocean linear internal wave. While underwater, individual drogues do not have access to absolute position information and can only rely on inter-drogue measurements. Building on this data and the knowledge of the drogue dynamics under the flow induced by the internal wave, we propose the Vanishing Distance Derivative Detection Strategy to allow individual drogues to determine the wave parameters. We analyze the correctness and robustness of this strategy under noiseless and noisy measurements, respectively. We also introduce a general methodology, termed $p$ th-Order Parameter Fusion, for combining the parameter estimates obtained at different times and characterize its error. Simulations illustrate our results.

Description: A study on filtering aspects of airborne wind energy generators is presented. This class of renewable energy systems aim to convert the aerodynamic forces generated by tethered wings, flying in closed paths transverse to the wind flow, into electricity. The accurate reconstruction of the wing's position, velocity, and heading is of fundamental importance for the automatic control of these kinds of systems. The difficulty of the estimation problem arises from the nonlinear dynamics, wide speed range, large accelerations, and fast changes of direction that the wing experiences during operation. It is shown that the overall nonlinear system has a specific structure allowing its partitioning into subsystems, hence leading to a series of simpler filtering problems. Different sensor setups are then considered, and the related sensor fusion algorithms are presented. The results of experimental tests carried out with a small-scale prototype and wings of different sizes are discussed. The designed filtering algorithms rely purely on kinematic laws, hence they are independent of features such as wing area, aerodynamic efficiency, mass, and so on. Therefore, the presented results are representative for systems with larger size and different wing design, different number of tethers and/or rigid wings also.

Description: The potential applications of dynamically substructured systems (DSSs) with both numerical and physical substructures can be found in diverse dynamics testing fields. In this paper, an adaptive feedforward controller based on a neural network (NN) is proposed to improve the DSS testing performance. To facilitate the NN compensation design, a modified DSS framework is developed so that the DSS control can be considered as a regulation problem with disturbance rejection. Then an adaptive NN feedforward compensation technique is proposed to cope with uncertainties and nonlinearities in the DSS physical substructure. The proposed NN technique generalizes the existing results in the literature, and it does not require any information of the plant model and disturbance model, which significantly simplifies its application on DSS. In particular, we propose a novel adaptive law for the NN online learning, where appropriate NN weight error information is derived and used to achieve improved performance. Real-time experimental results on a mechanical test rig demonstrate the improved performance by using the NN compensation strategy and the new adaptation law.

Description: The stability of haptic rendering is affected by many factors that limit the range of impedances that can be applied to virtual objects. This paper addresses the effect of the vibration modes of the haptic interface on the stability of the impedance control loop. It is well known that experimental stability boundaries present complex shapes, making it difficult to predict the final $Z$ -width of the haptic system. This paper shows how the vibration modes of the mechanical interface highly affect the size of the $Z$ -width, causing a sudden reduction in the critical virtual stiffness $K$ if the virtual damping $B$ is increased beyond a certain value. The inclusion of the most significant vibration modes in the theoretical model of the haptic system—together with the viscous damping, time delay and sampling rate—makes it possible to obtain the stable impedances associated with the haptic device. A PHANToM Premium 1.0 haptic interface was used as a test bed to validate this paper. Although results have been tested only on this device, this paper proposes a methodology for obtaining the $Z$ -width that can be generalized for any other haptic system.

Description: This paper identifies and addresses the control challenges associated with simultaneous power and thermal management of a 5-kW-class solid oxide fuel cell and gas turbine combined cycle system. A model predictive controller (MPC) is developed to achieve improved system performance subject to input and state constraints. The subsystem dynamic couplings and control authority limitations under thermal constraints are investigated by both static and dynamic analysis. Through judicious allocation of penalty parameters in the cost function associated with the fuel cell current, anode fuel flow rate, and generator load, several different MPC implementations are derived to explore different control design degree of freedom and their performance is evaluated. Simulation results show the efficacy of the MPC design by demonstrating the fast load transition while maintaining the stack temperature within the allowable operation limits.

Description: In this paper, the use of multiple variable spaces is proposed for monitoring modern industrial processes where data for a large number of process variables may be collected from different sources to reveal different characteristics. The easiest method of modeling a process is to treat all variables in a single data space, but then the information inherent in different types of variables would be mixed together and there would be no local view of each variable space. An extended algorithm based on the concept of total projection to latent structures, which we call multispace T-PLS (MsT-PLS), is thus developed to treat variables in multiple data spaces. Multiple variable spaces that are separated from the measurement space are composed of different sets of process variables measured at the same time and responsible for the same response data. Using the proposed algorithm, the relationships among multiple variable spaces are studied under the supervision of quality characteristics. Thus, comprehensive information decomposition is obtained in each variable space, which can be separated into four systematic subspaces in response to the cross-space common and specific process variability and one final residual subspace. The theoretical support for MsT-PLS is analyzed in detail and its statistical characteristics are compared with those of single-space T-PLS (SsT-PLS) algorithm. A process monitoring strategy is developed based on the MsT-PLS subspace decomposition result and applied to the Tennessee Eastman process for illustration purposes.

Description: An improved direct power control (DPC) is proposed for a three-phase voltage-source rectifier based on fuzzy sliding mode. It introduces the sliding mode controller (SMC) to enhance the disturbance rejection ability of the outer dc voltage loop. To ensure the stability of SMC, a fuzzy-based switching state controller is developed for the inner power loop, with which the switching states can be provided directly at the fixed time interval. This can contribute to the digitalization of controller and eliminate the drawbacks of the predefined switching table and the variable frequency that exists in the classical DPC. The effectiveness of the proposed DPC is verified by the simulation and experiment. Compared with the classical DPC, the current total harmonic distortion in the steady state is reduced by nearly half, and the transient setting time is shortened by at least 23%. The results indicate better performance of the proposed DPC in both the steady and transient states.