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Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method
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DOI：10.1016/j.neucom.2019.02.034.png)
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Abstract
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In this paper, an online integral reinforcement learning strategy is proposed to deal with robust constrained control problems using event-triggered mechanism for nonlinear Continuous-Time (C-T) systems with external disturbances. The novel design of constrained control law is addressed together with the adaptive event-triggered condition by guaranteeing the optimal performance and system stability. An adaptive online actor-critic Neural Network (NN) reinforcement learning scheme is developed to approximate the optimal solution of the complicated Hamilton-Jacobi-Isaacs equation. Meanwhile, the convergence of NN weight errors and the event-triggered closed-loop system stability are demonstrated to be uniform ultimate bounded by Lyapunov analysis under the proposed triggering condition. Moreover, event-triggered H-infinity tracking control with input constrains and limited network communication is also presented by establishing an augmented system. Finally, simulation results are provided to show the algorithm validity. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Event-triggered control
Robust H-infinity control
Hamilton-Jacobi-Isaacs (HJI) equation
Neural networks
Input constrains
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