Neural Network Based Finite Horizon Optimal Control for a Class of Nonlinear Systems with State Delay and Control Constraints

被引：0
|
作者：
Lin, Xiaofeng [1 ]
Cao, Nuyun [1 ]
Lin, Yuzhang [2 ]
机构：
[1] Guangxi Univ, Sch Elect Engn, Nanning 530004, Peoples R China
[2] Tsinghua Univ, Dept Elect Engn, Beijing 100083, Peoples R China
来源：
2013 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2013年
基金：
中国国家自然科学基金;
关键词：
LINEAR-SYSTEMS; TIME;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new finite horizon iterative ADP algorithm is used to solve a class of nonlinear systems with state delay and control constraints problem and finite timee epsilon-optimal control is obtained. First of all, a new performance index function is designed to deal with the control constraints, the discrete nonlinear systems HJB equation with state delay is presented. Second, the iterative process and mathematical proof of the convergence is illustrated for the proposed finite horizon ADP algorithm. Approximate optimal control is obtained by introducing an error bonde epsilon. Two BP neural networks are developed to approximate control law function and performance index function in our ADP algorithm. Finally, comparing simulation cases are used to verify the effectiveness of the method proposed in this paper.
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页数：6
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