Scholarlink

<p>This paper considers the problem of selecting control laws with guaranteed closed-loop properties from a set of functions represented by deep neural networks or other parametric approximators. The task is addressed for time-varying linear systems with multiplicative uncertainties and polytopic state and input constraints. Based on ellipsoidal control-invariant and <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="40435_2025_1779_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\lambda \)</EquationSource> <EquationSource Format="MATHML"><math> <mi>λ</mi> </math></EquationSource> </InlineEquation>-contractive sets, which can be computed offline for this system class, the paper shows how safe and stabilizing sets of control laws can be defined and computed. In particular, a closed-form transformation of the control laws is proposed as a tailored output layer, ensuring the satisfaction of the constraints and asymptotic stability of the origin for all possible parameterizations.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Guaranteed constraint satisfaction and asymptotic closed-loop stability in learning of control laws

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月9日

查看全文
  • Lukas Markolf,
  • Olaf Stursberg

摘要

This paper considers the problem of selecting control laws with guaranteed closed-loop properties from a set of functions represented by deep neural networks or other parametric approximators. The task is addressed for time-varying linear systems with multiplicative uncertainties and polytopic state and input constraints. Based on ellipsoidal control-invariant and \(\lambda \) λ -contractive sets, which can be computed offline for this system class, the paper shows how safe and stabilizing sets of control laws can be defined and computed. In particular, a closed-form transformation of the control laws is proposed as a tailored output layer, ensuring the satisfaction of the constraints and asymptotic stability of the origin for all possible parameterizations.