Iterative GDHP-based approximate optimal tracking control for a class of discrete-time nonlinear systems

被引：43
|
作者：
Mu, Chaoxu [1 ,2 ]
Sun, Changyin [2 ]
Song, Aiguo [3 ]
Yu, Hualong [2 ]
机构：
[1] Tianjin Univ, Sch Elect Engn & Automat, Tianjin Key Lab Proc Measurement & Control, Tianjin 300072, Peoples R China
[2] Southeast Univ, Sch Automat, Nanjing 210096, Jiangsu, Peoples R China
[3] Southeast Univ, Dept Instrument Sci & Engn, Nanjing 210096, Jiangsu, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 214卷
基金：
中国国家自然科学基金;
关键词：
Adaptive dynamic programming (ADP); Approximate optimal tracking control; Globalized dual heuristic programming (GDHP); Neural networks; Nonlinear systems; ADAPTIVE CRITIC DESIGNS; NEURAL-NETWORKS; REINFORCEMENT; REGRESSION; ALGORITHM;
D O I：
10.1016/j.neucom.2016.06.059
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, an iterative globalized dual heuristic programming (GDHP) method is developed to deal with the approximate optimal tracking control for a class of discrete-time nonlinear systems. The optimal tracking control problem is formulated by solving the discrete-time Hamilton-Jacobi-Bellman (DTHJB) equation. Then, it is approximately solved by the developed iterative GDHP-based algorithm with convergence analysis. The iterative GDHP algorithm is implemented by constructing three neural networks to approximate the error system dynamics, the cost function with its derivative, and the control policy in each iteration, respectively. The information of the cost function and its derivative is provided during iteration calculation. Two simulation examples are investigated to verify the performance of the proposed approximate optimal tracking control approach. (C) 2016 Elsevier B.V. All rights reserved.
引用
收藏
页码：775 / 784
页数：10
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