Regular Papers

International Journal of Control, Automation and Systems 2022; 20(3): 857-868

Published online March 11, 2022

https://doi.org/10.1007/s12555-021-0210-1

© The International Journal of Control, Automation, and Systems

Adaptive Event Triggered Optimal Control for Constrained Continuous-time Nonlinear Systems

Ping Wang, Zhen Wang*, and Qian Ma

Shandong University of Science and Technology

Abstract

This paper considers the event-triggered optimal control (ETOC) strategy for constrained continuoustime nonlinear systems via adaptive dynamic programming (ADP). First, a novel event-triggering condition is proposed, which can guarantee the stability of the closed-loop system. Meanwhile, the existence of a lower bound for the execution time is proved, which can guarantee that the designed event-trigger scheme avoids Zeno behavior. Then, to solve the partial differential Hamilton-Jacobi-Bellman (HJB) equation,the critic Neural Network (NN) is designed to approximate the cost function. So that the ADP-based ETOC scheme is designed. Moreover, through Lyapunov stability analysis, the stability of the closed-loop system can be ensured. Also, the uniform ultimate boundedness of the states and the weight estimation error can also be guaranteed. Last, a numerical example is given to illustrate the effectiveness and advantages of the proposed control scheme.

Keywords ADP, constrained input, event-triggered, neural networks, optimal control.

Article

Regular Papers

International Journal of Control, Automation and Systems 2022; 20(3): 857-868

Published online March 1, 2022 https://doi.org/10.1007/s12555-021-0210-1

Copyright © The International Journal of Control, Automation, and Systems.

Adaptive Event Triggered Optimal Control for Constrained Continuous-time Nonlinear Systems

Ping Wang, Zhen Wang*, and Qian Ma

Shandong University of Science and Technology

Abstract

This paper considers the event-triggered optimal control (ETOC) strategy for constrained continuoustime nonlinear systems via adaptive dynamic programming (ADP). First, a novel event-triggering condition is proposed, which can guarantee the stability of the closed-loop system. Meanwhile, the existence of a lower bound for the execution time is proved, which can guarantee that the designed event-trigger scheme avoids Zeno behavior. Then, to solve the partial differential Hamilton-Jacobi-Bellman (HJB) equation,the critic Neural Network (NN) is designed to approximate the cost function. So that the ADP-based ETOC scheme is designed. Moreover, through Lyapunov stability analysis, the stability of the closed-loop system can be ensured. Also, the uniform ultimate boundedness of the states and the weight estimation error can also be guaranteed. Last, a numerical example is given to illustrate the effectiveness and advantages of the proposed control scheme.

Keywords: ADP, constrained input, event-triggered, neural networks, optimal control.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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