Event-triggered robust model predictive control of continuous-time nonlinear systems

The event-triggered control is of compelling features in efficiently exploiting system resources, and thus has found many applications in sensor networks, networked control systems, multi-agent systems and so on. In this paper, we study the event-triggered model predictive control (MPC) problem for...

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Bibliographic Details
Published in:	Automatica (Oxford) Vol. 50; no. 5; pp. 1507 - 1513
Main Authors:	Li, Huiping, Shi, Yang
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.05.2014 Elsevier
Subjects:	Adaptative systems Algorithms Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Continuous-time systems Control system analysis Control theory. Systems Disturbances Dynamical systems Event-triggered Exact sciences and technology Feasibility Mathematical models Model predictive control (MPC) Nonlinear dynamics Nonlinear systems Optimal control Predictive control Robust control Software Stability Robust control Model predictive control (MPC) Nonlinear systems Continuous-time systems Event-triggered Disturbances Event management Non linear control Continuous time Invariant set Dynamical system Non linear system Dead beat control Model predictive control Reactive system Multiagent system Mechanism synthesis Distributed control Feasibility Sensor array Triggering Discrete event system
ISSN:	0005-1098, 1873-2836
Online Access:	Get full text
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Abstract	The event-triggered control is of compelling features in efficiently exploiting system resources, and thus has found many applications in sensor networks, networked control systems, multi-agent systems and so on. In this paper, we study the event-triggered model predictive control (MPC) problem for continuous-time nonlinear systems subject to bounded disturbances. An event-triggered mechanism is first designed by measuring the error between the system state and its optimal prediction; the event-triggered MPC algorithm that is built upon the triggering mechanism and the dual-mode approach is then designed. The rigorous analysis of the feasibility and stability is conducted, and the sufficient conditions for ensuring the feasibility and stability are developed. We show that the feasibility of the event-triggered MPC algorithm can be guaranteed if, the prediction horizon is designed properly and the disturbances are small enough. Furthermore, it is shown that the stability is related to the prediction horizon, the disturbance bound and the triggering level, and that the state trajectory converges to a robust invariant set under the proposed conditions. Finally, a case study is provided to verify the theoretical results.
AbstractList	The event-triggered control is of compelling features in efficiently exploiting system resources, and thus has found many applications in sensor networks, networked control systems, multi-agent systems and so on. In this paper, we study the event-triggered model predictive control (MPC) problem for continuous-time nonlinear systems subject to bounded disturbances. An event-triggered mechanism is first designed by measuring the error between the system state and its optimal prediction; the event-triggered MPC algorithm that is built upon the triggering mechanism and the dual-mode approach is then designed. The rigorous analysis of the feasibility and stability is conducted, and the sufficient conditions for ensuring the feasibility and stability are developed. We show that the feasibility of the event-triggered MPC algorithm can be guaranteed if, the prediction horizon is designed properly and the disturbances are small enough. Furthermore, it is shown that the stability is related to the prediction horizon, the disturbance bound and the triggering level, and that the state trajectory converges to a robust invariant set under the proposed conditions. Finally, a case study is provided to verify the theoretical results.
Author	Li, Huiping Shi, Yang
Author_xml	– sequence: 1 givenname: Huiping surname: Li fullname: Li, Huiping email: lihuiping@nwpu.edu.cn organization: School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an, 710072, China – sequence: 2 givenname: Yang surname: Shi fullname: Shi, Yang email: yshi@uvic.ca organization: Department of Mechanical Engineering, University of Victoria, Victoria, B.C., Canada, V8W 3P6
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Keywords	Robust control Model predictive control (MPC) Nonlinear systems Continuous-time systems Event-triggered Disturbances Event management Non linear control Continuous time Invariant set Dynamical system Non linear system Dead beat control Model predictive control Reactive system Multiagent system Mechanism synthesis Distributed control Feasibility Sensor array Triggering Discrete event system
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SubjectTerms	Adaptative systems Algorithms Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Continuous-time systems Control system analysis Control theory. Systems Disturbances Dynamical systems Event-triggered Exact sciences and technology Feasibility Mathematical models Model predictive control (MPC) Nonlinear dynamics Nonlinear systems Optimal control Predictive control Robust control Software Stability
Title	Event-triggered robust model predictive control of continuous-time nonlinear systems
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