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Frank Allgöwer - One of the best experts on this subject based on the ideXlab platform.
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Robust self-triggered MPC for constrained linear systems
Automatica, 2016Co-Authors: Florian D. Brunner, Maurice Heemels, Frank AllgöwerAbstract:We propose a robust self-triggered control algorithm for constrained linear discrete-time systems subject to additive disturbances based on MPC. At every Sampling Instant, the controller provides both the next Sampling Instant, as well as the inputs that are applied to the system until the next Sampling Instant. By maximizing the inter-Sampling time subject to bounds on the MPC value function, the average Sampling frequency in the closed-loop system is decreased while guaranteeing an upper bound on the performance loss when compared with an MPC scheme Sampling at every point in time. Robust constraint satisfaction is achieved by tightening input and state constraints based on a Tube MPC approach. Moreover, a compact set in the state space, which is a parameter in the MPC scheme, is shown to be robustly asymptotically stabilized.
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Numerical Evaluation of a Robust Self-Triggered MPC Algorithm*
IFAC-PapersOnLine, 2016Co-Authors: Florian D. Brunner, Wpmh Maurice Heemels, Frank AllgöwerAbstract:Abstract: We present numerical examples demonstrating the efficacy of a recently proposed self-triggered model predictive control scheme for disturbed linear discrete-time systems with hard constraints on the input and state. In order to reduce the amount of communication between the controller and the actuator, the control input is not re-computed at each point in time but only at certain Sampling instances. These instances are determined in a self-triggered fashion in the sense that at every Sampling Instant the next Sampling Instant is computed as a function of the current system state. A compact set in the state space, whose size is a design parameter in the control scheme, is stabilized.
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RobustSelf-TriggeredMPCforConstrainedLinearSystems: ATube-BasedApproach ?
2015Co-Authors: Florian D. Brunner, Wpmh Maurice Heemels, Frank AllgöwerAbstract:We propose a robust self-triggered control algorithm for constrained linear discrete-time systems subject to additive disturbances based on MPC. At every Sampling Instant, the controller provides both the next Sampling Instant, as well as the inputs that are applied to the system until the next Sampling Instant. By maximizing the inter-Sampling time subject to bounds on the MPC value function, the average Sampling frequency in the closed-loop system is decreased while guaranteeing an upper bound on the performance loss when compared with an MPC scheme Sampling at every point in time. Robust constraint satisfaction is achieved by tightening input and state constraints based on a Tube MPC approach. Moreover, a compact set in the state space, which is a parameter in the MPC scheme, is shown to be robustly asymptotically stabilized.
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ECC - Robust self-triggered model predictive control for constrained discrete-time LTI systems based on homothetic tubes
2015 European Control Conference (ECC), 2015Co-Authors: Emre Aydiner, Wpmh Maurice Heemels, Florian D. Brunner, Frank AllgöwerAbstract:In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every Sampling Instant an optimization problem based on the current state of the system is solved in order to determine the input applied to the system until the next Sampling Instant, as well as the next Sampling Instant itself. This leads to inter-Sampling times that depend on the trajectory of the system. By maximizing the inter-Sampling time, the amount of communication in the control system is reduced. In order to guarantee robust constraint satisfaction, Tube MPC methods are employed. Specifically, in order to account for the uncertainty in the system, homothetic sets are used in the prediction of the future evolution of the system. The proposed controller is shown to stabilize a closed and bounded set including the origin in its interior.
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Nonlinear Model Predictive Control and Sum of Squares Techniques
Lecture Notes in Control and Information Sciences, 2006Co-Authors: Tobias Raff, Rolf Findeisen, Christian Ebenbauer, Frank AllgöwerAbstract:The paper considers the use of sum of squares techniques in nonlinear model predictive control. To be more precise, sum of squares techniques are used to solve at each Sampling Instant a finite horizon optimal control problem which arises in nonlinear model predictive control for discrete time polynomial systems. The combination of nonlinear model predictive control and sum of squares techniques is motivated by the successful application of semidefinite programming in linear model predictive control. The advantages and disadvantages of applying sum of squares techniques to nonlinear model predictive control are illustrated on a small example.
Florian D. Brunner - One of the best experts on this subject based on the ideXlab platform.
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Robust self-triggered MPC for constrained linear systems
Automatica, 2016Co-Authors: Florian D. Brunner, Maurice Heemels, Frank AllgöwerAbstract:We propose a robust self-triggered control algorithm for constrained linear discrete-time systems subject to additive disturbances based on MPC. At every Sampling Instant, the controller provides both the next Sampling Instant, as well as the inputs that are applied to the system until the next Sampling Instant. By maximizing the inter-Sampling time subject to bounds on the MPC value function, the average Sampling frequency in the closed-loop system is decreased while guaranteeing an upper bound on the performance loss when compared with an MPC scheme Sampling at every point in time. Robust constraint satisfaction is achieved by tightening input and state constraints based on a Tube MPC approach. Moreover, a compact set in the state space, which is a parameter in the MPC scheme, is shown to be robustly asymptotically stabilized.
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Numerical Evaluation of a Robust Self-Triggered MPC Algorithm*
IFAC-PapersOnLine, 2016Co-Authors: Florian D. Brunner, Wpmh Maurice Heemels, Frank AllgöwerAbstract:Abstract: We present numerical examples demonstrating the efficacy of a recently proposed self-triggered model predictive control scheme for disturbed linear discrete-time systems with hard constraints on the input and state. In order to reduce the amount of communication between the controller and the actuator, the control input is not re-computed at each point in time but only at certain Sampling instances. These instances are determined in a self-triggered fashion in the sense that at every Sampling Instant the next Sampling Instant is computed as a function of the current system state. A compact set in the state space, whose size is a design parameter in the control scheme, is stabilized.
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RobustSelf-TriggeredMPCforConstrainedLinearSystems: ATube-BasedApproach ?
2015Co-Authors: Florian D. Brunner, Wpmh Maurice Heemels, Frank AllgöwerAbstract:We propose a robust self-triggered control algorithm for constrained linear discrete-time systems subject to additive disturbances based on MPC. At every Sampling Instant, the controller provides both the next Sampling Instant, as well as the inputs that are applied to the system until the next Sampling Instant. By maximizing the inter-Sampling time subject to bounds on the MPC value function, the average Sampling frequency in the closed-loop system is decreased while guaranteeing an upper bound on the performance loss when compared with an MPC scheme Sampling at every point in time. Robust constraint satisfaction is achieved by tightening input and state constraints based on a Tube MPC approach. Moreover, a compact set in the state space, which is a parameter in the MPC scheme, is shown to be robustly asymptotically stabilized.
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ECC - Robust self-triggered model predictive control for constrained discrete-time LTI systems based on homothetic tubes
2015 European Control Conference (ECC), 2015Co-Authors: Emre Aydiner, Wpmh Maurice Heemels, Florian D. Brunner, Frank AllgöwerAbstract:In this paper we present a robust self-triggered model predictive control (MPC) scheme for discrete-time linear time-invariant systems subject to input and state constraints and additive disturbances. In self-triggered model predictive control, at every Sampling Instant an optimization problem based on the current state of the system is solved in order to determine the input applied to the system until the next Sampling Instant, as well as the next Sampling Instant itself. This leads to inter-Sampling times that depend on the trajectory of the system. By maximizing the inter-Sampling time, the amount of communication in the control system is reduced. In order to guarantee robust constraint satisfaction, Tube MPC methods are employed. Specifically, in order to account for the uncertainty in the system, homothetic sets are used in the prediction of the future evolution of the system. The proposed controller is shown to stabilize a closed and bounded set including the origin in its interior.
Yuanqing Xia - One of the best experts on this subject based on the ideXlab platform.
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Stochastic self-triggered model predictive control for linear systems with probabilistic constraints
Automatica, 2018Co-Authors: Li Dai, Lihua Xie, Yulong Gao, Karl Henrik Johansson, Yuanqing XiaAbstract:A stochastic self-triggered model predictive control (SSMPC) algorithm is proposed for linear systems subject to exogenous disturbances and probabilistic constraints. The main idea behind the self-triggered framework is that at each Sampling Instant, an optimization problem is solved to determine both the next Sampling Instant and the control inputs to be applied between the two Sampling Instants. Although the self-triggered implementation achieves communication reduction, the control commands are necessarily applied in open-loop between Sampling Instants. To guarantee probabilistic constraint satisfaction, necessary and sufficient conditions are derived on the nominal systems by using the information on the distribution of the disturbances explicitly. Moreover, based on a tailored terminal set, a multi-step open-loop MPC optimization problem with infinite prediction horizon is transformed into a tractable quadratic programming problem with guaranteed recursive feasibility. The closed-loop system is shown to be stable. Numerical examples illustrate the efficacy of the proposed scheme in terms of performance, constraint satisfaction, and reduction of both control updates and communications with a conventional time-triggered scheme.
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constrained infinite horizon model predictive control for fuzzy discrete time systems
IEEE Transactions on Fuzzy Systems, 2010Co-Authors: Yuanqing Xia, Hongjiu Yang, Peng ShiAbstract:The problem of constrained infinite-horizon model-predictive control for fuzzy-discrete systems is considered in this paper. New sufficient conditions are proposed in terms of linear-matrix inequalities. Based on the optimal solutions of these sufficient conditions at each Sampling Instant, we design both parallel-distributed compensation and nonparallel-distributed compensation state-feedback controllers, which can guarantee that the resulting closed-loop fuzzy-discrete system is asymptotically stable. In addition, the fuzzy-feedback controllers meet the specifications for the fuzzy-discrete systems with both input and output constraints. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques.
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Robust Parameter-Dependent Constrained Model Predictive Control
Second International Conference on Innovative Computing Informatio and Control (ICICIC 2007), 2007Co-Authors: Yuanqing Xia, J. Chen, Peng Shi, G.p. LiuAbstract:The problem of robust constrained model predictive control (MFC) of systems with polytopic uncertainty is considered in this paper. New sufficient conditions for the existence of parameter-dependent Lyapunov functions are proposed in terms of linear matrix inequalities (LMIs), which will reduce the conservativeness resulting from using a single Lyapunov function. At each Sampling Instant, the corresponding parameter-dependent Lyapunov function is an upper bound for a worst-case objective function, which can be minimized using the LMI convex optimization approach. Based on the solution of optimization at each Sampling Instant, the corresponding state feedback controller is designed, which can guarantee that the resulting closed-loop system is robustly asymptotically stable. In addition, the feedback controller will meet the specifications for systems with input or output constraints, for all admissible time-varying parameter uncertainties. Numerical examples are presented to demonstrate the effectiveness of the proposed techniques.
Basil Kouvaritakis - One of the best experts on this subject based on the ideXlab platform.
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an active set solver for input constrained robust receding horizon control
Automatica, 2014Co-Authors: Johannes Buerger, Mark Cannon, Basil KouvaritakisAbstract:An efficient optimization procedure is proposed for computing a receding horizon control law for linear systems with linearly constrained control inputs and additive disturbances. The procedure uses an active set approach to solve the dynamic programming problem associated with the min-max optimization of an H ∞ performance index. The active constraint set is determined at each Sampling Instant using first-order necessary conditions for optimality. The computational complexity of each iteration of the algorithm depends linearly on the prediction horizon length. We discuss convergence, closed loop stability and bounds on the disturbance l 2 -gain in closed loop operation.
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an active set solver for input constrained robust receding horizon control
Conference on Decision and Control, 2011Co-Authors: Johannes Buerger, Mark Cannon, Basil KouvaritakisAbstract:An efficient optimization procedure is proposed for computing a receding horizon control law for linear systems with constrained control inputs and additive disturbances. The procedure uses an active set method to solve the dynamic programming problem associated with the min-max optimization of a predicted cost. The active set at the solution is determined at each Sampling Instant as a function of the current system state using the first-order necessary conditions for optimality. The computational complexity of each iteration is linear in the length of the prediction horizon. We discuss conditions for stability and bounds on state and input l 2 -norms in closed loop operation.
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Robust tubes in nonlinear model predictive control
IFAC Proceedings Volumes, 2010Co-Authors: Mark Cannon, Johannes Buerger, Basil Kouvaritakis, Sasa V. RakovicAbstract:Abstract Nonlinear model predictive control (NMPC) strategies based on linearization about predicted system trajectories enable the online NMPC optimization to be performed by a sequence of convex optimization problems. The approach relies on bounds on linearization errors in order to ensure constraint satisfaction and convergence of the performance index during the optimization at each Sampling Instant and along closed loop system trajectories. This paper proposes bounds based on robust tubes constructed around predicted trajectories. To ensure local optimality, the bounds are non-conservative for the case of zero linearization error, which requires the tube cross-sections to vary along predicted trajectories. The feasibility, stability and convergence properties of the algorithm are established without the need for predictions to satisfy local optimality criteria. The strategy is applied to a simulated fixed-rotor helicopter.
Qing-long Han - One of the best experts on this subject based on the ideXlab platform.
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Event-Triggered Dynamic Positioning for Mass-Switched Unmanned Marine Vehicles in Network Environments.
IEEE transactions on cybernetics, 2020Co-Authors: Yu-long Wang, Qing-long HanAbstract:This article is concerned with event-triggered dynamic positioning for a mass-switched unmanned marine vehicle (UMV) in network environments. First, a switched dynamic positioning system (DPS) model for a mass-switched marine vehicle is established. The switched DPS model takes into consideration changes in the marine vehicle's mass and the resultant switching of the marine vehicle's parameters. Second, for a mass-switched UMV controlled through a communication network, a novel weighted event-triggering communication scheme considering switching features is proposed. The weighted error data of multiple Sampling Instants are utilized to avoid a long-time nontriggering phenomenon. The consideration of switching features guarantees the current sampled data to be transmitted if a switch occurs between the last Sampling Instant and the current Sampling Instant. Then, under the event-triggering scheme, an asynchronously switched DPS model for the mass-switched UMV is established in network environments. Based on this model, a mode-dependent DPS controller and event generator co-design method are proposed to attenuate the disturbance induced by wind, waves, and ocean currents. The DPS performance analysis demonstrates the effectiveness of the proposed method.
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on designing a novel self triggered Sampling scheme for networked control systems with data losses and communication delays
IEEE Transactions on Industrial Electronics, 2016Co-Authors: Chen Peng, Qing-long HanAbstract:A self-triggered Sampling scheme (STS) is proposed for a networked control system with consideration of data losses and communication delays. By making use of this scheme, the next Sampling Instant does not depend on online estimation of an event-triggered condition and the successive measurement of the state, and can be dynamically determined with respect to the transmitted packet, the desired control performance, and the allowable number of consecutive data losses and communication delays. Consequently, the Sampling interval can be adaptively adjusted. Therefore, the communication burden can be greatly reduced and the energy efficiency can be much improved while preserving the desired ${H_\infty}$ performance. An inverted pendulum and a one-area power system controlled over a wireless sensor network are given to illustrate the effectiveness of the proposed STS.
