Neural-network-based approach to finite-time optimal control for a class of unknown nonlinear systems

被引：14

作者：

Song, Ruizhuo ^{[1
]}

Xiao, Wendong ^{[1
]}

Wei, Qinglai ^{[2
]}

Sun, Changyin ^{[1
]}

机构：

[1] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China

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

来源：

SOFT COMPUTING | 2014年 / 18卷 / 08期

基金：

中国博士后科学基金; 北京市自然科学基金; 中国国家自然科学基金;

关键词：

Adaptive dynamic programming; Approximate dynamic programming; Unknown nonlinear systems; Optimal control; Data-based; EXTREME LEARNING-MACHINE; TRACKING CONTROL; CONTROL SCHEME; APPROXIMATE; CAPABILITIES;

D O I：

10.1007/s00500-013-1170-z

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a novel finite-time optimal control method based on input-output data for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. In this method, the single-hidden layer feed-forward network (SLFN) with extreme learning machine (ELM) is used to construct the data-based identifier of the unknown system dynamics. Based on the data-based identifier, the finite-time optimal control method is established by ADP algorithm. Two other SLFNs with ELM are used in ADP method to facilitate the implementation of the iterative algorithm, which aim to approximate the performance index function and the optimal control law at each iteration, respectively. A simulation example is provided to demonstrate the effectiveness of the proposed control scheme.

引用

页码：1645 / 1653

页数：9

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