Sub-optimal scheduling in switched systems with continuous-time dynamics: A gradient descent approach

被引：10

作者：

Sardarmehni, Tohid ^{[1
]}

Heydari, Ali ^{[1
]}

机构：

[1] Southern Methodist Univ, Dept Mech Engn, Dallas, TX 75205 USA

来源：

NEUROCOMPUTING | 2018年 / 285卷

基金：

美国国家科学基金会;

关键词：

Optimal control; Switched systems; Policy iteration; Continuous-time dynamics; NONLINEAR-SYSTEMS; STABILITY; TRACKING; POLICY;

D O I：

10.1016/j.neucom.2018.01.003

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A feedback solution for approximate optimal scheduling of switched systems with autonomous subsystems and continuous-time dynamics is presented. The proposed solution is based on policy iteration algorithm which provides the optimal switching schedule. Algorithms for offline, online, and concurrent implementation of the proposed solution are presented. For online and concurrent training, gradient descent training laws are used and the performance of the training laws is analyzed. The effectiveness of the presented algorithms is verified through numerical simulations. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：10 / 22

页数：13

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