Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics

被引：840

作者：

Jiang, Yu ^{[1
]}

Jiang, Zhong-Ping ^{[1
]}

机构：

[1] NYU, Dept Elect & Comp Engn, Polytech Inst, Brooklyn, NY 11201 USA

来源：

AUTOMATICA | 2012年 / 48卷 / 10期

基金：

美国国家科学基金会;

关键词：

Adaptive optimal control; Policy iterations; Linear-quadratic regulator (LQR); ZERO-SUM GAMES; FEEDBACK-CONTROL;

D O I：

10.1016/j.automatica.2012.06.096

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 paper presents a novel policy iteration approach for finding online adaptive optimal controllers for continuous-time linear systems with completely unknown system dynamics. The proposed approach employs the approximate/adaptive dynamic programming technique to iteratively solve the algebraic Riccati equation using the online information of state and input, without requiring the a priori knowledge of the system matrices. In addition, all iterations can be conducted by using repeatedly the same state and input information on some fixed time intervals. A practical online algorithm is developed in this paper, and is applied to the controller design for a turbocharged diesel engine with exhaust gas recirculation. Finally, several aspects of future work are discussed. (C) 2012 Elsevier Ltd. All rights reserved.

引用

页码：2699 / 2704

页数：6

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