Finite Horizon Optimal Tracking Control for a Class of Discrete-Time Nonlinear Systems

被引：0
|
作者：
Wei, Qinglai [1 ]
Wang, Ding [1 ]
Liu, Derong [1 ]
机构：
[1] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
来源：
ADVANCES IN NEURAL NETWORKS - ISNN 2011, PT II | 2011年 / 6676卷
关键词：
Adaptive dynamic programming; approximate dynamic programming; optimal tracking control; neural networks; finite horizon; PERFORMANCE INDEX; CONTROL SCHEME;
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 iterative ADP algorithm is proposed to solve the finite horizon optimal tracking control problem for a class of discrete-time nonlinear systems. The idea is that using system transformation, the optimal tracking problem is transformed into optimal regulation problem, and then the iterative ADP algorithm is introduced to deal with the regulation problem with convergence guarantee. Three neural networks are used to approximate the performance index function, compute the optimal control policy and model the unknown system dynamics, respectively, for facilitating the implementation of iterative ADP algorithm. An example is given to demonstrate the validity of the proposed optimal tracking control scheme.
引用
收藏
页码：620 / 629
页数：10
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