Robust Adaptive Neural Network Optimal Control for a Class of Uncertain Nonlinear Systems

被引：0
|
作者：
Yun, Xiaoyan [1 ]
Wu, Libing [2 ]
Xu, Yang [1 ]
Cheng, Chong [3 ]
Zhang, Xiaoxia [1 ]
机构：
[1] Univ Sci & Technol Liaoning, Sch Software, Anshan 114051, Liaoning, Peoples R China
[2] Univ Sci & Technol Liaoning, Sch Sci, Anshan 114051, Liaoning, Peoples R China
[3] Acad Anal Sci Liaoning, Shenyang 110000, Liaoning, Peoples R China
来源：
PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年
关键词：
Adaptive control; optimal control; nonlinear function; neural network; asymptotically stable; FEEDBACK-CONTROL; OUTPUT-FEEDBACK; LINEAR-SYSTEMS; TRACKING;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This study is concerned with the robust adaptive neural network optimal control problem for a class of nonlinear systems with parameter uncertainties and external disturbances. By using neural network (NN) to approximate the unknown nonlinear function within a suitable compact set, a novel adaptive neural network optimal controller with the adjustable parameter updated laws is designed. Furthermore, based on the Lyapunov stability theory, it is shown that the proposed robust adaptive optimal strategy can guarantee that the closed-loop system is asymptotically stable, and the performance of the design shows excellent consistency. Finally, simulation results are provided to illustrate the effectiveness of the proposed robust adaptive optimal control scheme.
引用
收藏
页码：521 / 526
页数：6
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