Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8947370 | Information Sciences | 2018 | 14 Pages |
Abstract
In this paper, we propose an output-based tracking control scheme for a class of continuous-time nonlinear systems via the adaptive dynamic programming (ADP) technique. A neural networks (NNs) observer is constructed to reconstruct immeasurable information of the nonlinear systems, and, by introducing a new state vector and appropriate coordinate transformation, tracking control issues are converted into optimal regulation problems where critic-actor neural networks structures are developed for the solution of Hamilton-Jacobi-Bellman (HJB) equation corresponding to tracking errors. In addition, a robust term is introduced to eliminate effects from approximation errors. It is proven that all signals in the closed-loop system are uniformly ultimately bounded (UUB) by the Lyapunov approach. Finally, simulation examples are provided for illustration of the theoretical claims.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yang Yang, Chuang Xu, Dong Yue, Xiangpeng Xie,