Learning finite-horizon optimal control with unknown control-affine dynamics

IF 2.1 3区计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS

Systems & Control Letters Pub Date : 2025-06-16 DOI:10.1016/j.sysconle.2025.106161

Yuqing Chen , Yangzhi Li , David J. Braun

引用次数: 0

Abstract

This paper introduces a method for learning finite-horizon optimal control for systems with control-affine dynamics without using the model of the system dynamics. We approximate the time- and state-dependent optimal control policy using model-free relearning of simple linear control policies. Assuming persistent excitation, we prove the convergence and optimality of the proposed learning method and demonstrate its use through a numerical example.

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具有未知控制仿射动力学的学习有限视界最优控制

本文介绍了一种不使用系统动力学模型学习控制仿射动力学系统的有限视界最优控制的方法。我们使用简单线性控制策略的无模型再学习来近似时间和状态相关的最优控制策略。在假设持续激励的情况下，我们证明了所提出的学习方法的收敛性和最优性，并通过一个数值例子说明了它的应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Systems & Control Letters 工程技术-运筹学与管理科学

CiteScore

4.60

自引率

3.80%

发文量

144

审稿时长

6 months

期刊介绍： Founded in 1981 by two of the pre-eminent control theorists, Roger Brockett and Jan Willems, Systems & Control Letters is one of the leading journals in the field of control theory. The aim of the journal is to allow dissemination of relatively concise but highly original contributions whose high initial quality enables a relatively rapid review process. All aspects of the fields of systems and control are covered, especially mathematically-oriented and theoretical papers that have a clear relevance to engineering, physical and biological sciences, and even economics. Application-oriented papers with sophisticated and rigorous mathematical elements are also welcome.