Neural-network-based adaptive guaranteed cost control of nonlinear dynamical systems with matched uncertainties

被引：29

作者：

Mu, Chaoxu ^{[1
]}

Wang, Ding ^{[2
]}

机构：

[1] Tianjin Univ, Tianjin Key Lab Proc Measurement & Control, Sch Elect & Informat Engn, Tianjin 300072, Peoples R China

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

来源：

NEUROCOMPUTING | 2017年 / 245卷

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

Adaptive dynamic programming (ADP); Guaranteed cost control; Neural networks; Uncertain dynamics; Stability; DISCRETE-TIME-SYSTEMS; SWITCHED NEUTRAL SYSTEMS; STABILIZATION; ALGORITHM; DESIGN;

D O I：

10.1016/j.neucom.2017.03.047

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, we investigate the neural-network-based adaptive guaranteed cost control for continuous time affine nonlinear systems with dynamical uncertainties. Through theoretical analysis, the guaranteed cost control problem is transformed into designing an optimal controller of the associated nominal system with a newly defined cost function. The approach of adaptive dynamic programming (ADP) is involved to implement the guaranteed cost control strategy with the neural network approximation. The stability of the closed-loop system with the guaranteed cost control law, the convergence of the critic network weights and the approximate boundary of the guaranteed cost control law are all analyzed. Two simulation examples have been conducted and all simulation results have indicated the good performance of the developed guaranteed cost control strategy. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：46 / 54

页数：9

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