NDLI: Adaptive Event-Triggered Near-Optimal Tracking Control for Unknown Continuous-Time Nonlinear Systems

Content Provider	Directory of Open Access Journals (DOAJ)
Author	Kunfu Wang Qijia Gu Baiqiao Huang Qinglai Wei Tianmin Zhou
Abstract	This paper studies the event-triggered optimal tracking control (ETOTC) problem of continuous-time (CT) unknown nonlinear systems. In order to solve the ETOTC problem, an augmented system composed of the error system dynamics and the reference dynamics is used to introduce a new discounted performance index function (DPIF). A novel event-triggered (ET) adaptive dynamic programming (ADP) method is developed to solve the ET Hamilton-Jacobi-Bellman equation (HJBE). The presented method is implemented via an identifier-critic architecture, which consists of two neural networks (NNs): an identifier NN is applied to estimate the unknown system dynamics, and a critic NN is constructed to obtain the approximate solution of the ET HJBE. The augmented closed-loop system and the critic estimation error are proved to be ultimately uniformly bounded (UUB) by the Lyapunov direct method. Finally, two simulations illustrate the effectiveness of the developed method.
e-ISSN	21693536
DOI	10.1109/ACCESS.2021.3140076
Journal	IEEE Access
Volume Number	10
Language	English
Publisher	IEEE
Publisher Date	2022-01-01
Publisher Place	United States
Access Restriction	Open
Subject Keyword	Electrical Engineering. Electronics. Nuclear Engineering Adaptive Dynamic Programming Event-triggered Mechanism Optimal Tracking Control Nonlinear System
Content Type	Text
Resource Type	Article

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Adaptive Event-Triggered Near-Optimal Tracking Control for Unknown Continuous-Time Nonlinear Systems