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- W2915777777 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." @default.
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- W2915777777 date "2019-05-01" @default.
- W2915777777 modified "2023-09-25" @default.
- W2915777777 title "Event-trigger-based robust control for nonlinear constrained-input systems using reinforcement learning method" @default.
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- W2915777777 doi "https://doi.org/10.1016/j.neucom.2019.02.034" @default.
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