An iterative adaptive dynamic programming algorithm for optimal control of unknown discrete-time nonlinear systems with constrained inputs

被引：107

作者：

Liu, Derong ^{[1
]}

Wang, Ding ^{[1
]}

Yang, Xiong ^{[1
]}

机构：

[1] Chinese Acad, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China

来源：

INFORMATION SCIENCES | 2013年 / 220卷

基金：

北京市自然科学基金; 中国国家自然科学基金; 中国博士后科学基金;

关键词：

Adaptive dynamic programming; Approximate dynamic programming; Control constraints; Globalized dual heuristic programming; Neural networks; Optimal control; NEURAL-NETWORKS; CONTROL DESIGN; REINFORCEMENT; LMI;

D O I：

10.1016/j.ins.2012.07.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, the adaptive dynamic programming (ADP) approach is employed for designing an optimal controller of unknown discrete-time nonlinear systems with control constraints. A neural network is constructed for identifying the unknown dynamical system with stability proof. Then, the iterative ADP algorithm is developed to solve the optimal control problem with convergence analysis. Two other neural networks are introduced for approximating the cost function and its derivatives and the control law, under the framework of globalized dual heuristic programming technique. Furthermore, two simulation examples are included to verify the theoretical results. (C) 2012 Elsevier Inc. All rights reserved.

引用

页码：331 / 342

页数：12

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