Finite-horizon optimal control of unknown nonlinear time-delay systems

被引：10
|
作者：
Cui, Xiaohong [1 ,2 ]
Zhang, Huaguang [1 ]
Luo, Yanhong [1 ]
Jiang, He [1 ]
机构：
[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China
[2] Mudanjiang Normal Univ, Inst Math Sci, Mudanjiang 157011, Heilongjiang, Peoples R China
来源：
NEUROCOMPUTING | 2017年 / 238卷
基金：
中国国家自然科学基金;
关键词：
Optimal control; Neural network; Time-delay; Finite-horizon; HJB equation; ADAPTIVE OPTIMAL-CONTROL; OPTIMAL-CONTROL DESIGN; POLICY ITERATION; LINEAR-SYSTEMS; NEURAL-CONTROL; DYNAMICS;
D O I：
10.1016/j.neucom.2017.01.063
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 propose a neural-network (NN)-based online off-policy algorithm to optimize a class of nonlinear continuous-time time-delay systems during finite time horizon. The online off-policy algorithm is used to learn the two-stage solution to the time-varying Hamilton-Jacobi-Bellman (HJB) equation without requiring the knowledge of the time-delay system dynamics. The algorithm is implemented by using an actor-critic NN structure with time-varying activation functions. The weights of the two NNs are tuned simultaneously in real-time by considering both the residual error and the terminal error. Two simulation examples demonstrate the applicability of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：277 / 285
页数：9
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