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Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method.
- Source :
-
Neurocomputing . May2019, Vol. 340, p158-170. 13p. - Publication Year :
- 2019
-
Abstract
- Abstract 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 ∞ 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 09252312
- Volume :
- 340
- Database :
- Academic Search Index
- Journal :
- Neurocomputing
- Publication Type :
- Academic Journal
- Accession number :
- 135376603
- Full Text :
- https://doi.org/10.1016/j.neucom.2019.02.034