Scholarlink

<p>This paper addresses the problem of efficiently solving differential linear matrix inequalities (DLMIs) that arise in the analysis and synthesis of control systems. A new numerical method based on a piecewise-quadratic (PWQ) parameterizations is developed. The proposed technique transforms a DLMI into tractable linear matrix inequalities (LMIs) that can be solved efficiently using semidefinite programming. The method is then applied to robust sampled-data control problems, yielding new tractable sufficient conditions for designing <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\({\mathcal {H}}_{\infty }\)</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi mathvariant="script">H</mi> <mi>∞</mi> </msub> </math></EquationSource> </InlineEquation> sampled-data state-feedback controllers for linear time-invariant (LTI) systems. The effectiveness of the proposed approach is demonstrated through numerical simulations, showing improved performance and reduced conservatism compared with existing methods.</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

New differential LMI solutions for sampled-data control systems

  • 期刊论文

  • 发表日期：

    2025年11月26日

查看全文
  • B. Bhiri,
  • M. Zasadzinski,
  • C. Delattre

摘要

This paper addresses the problem of efficiently solving differential linear matrix inequalities (DLMIs) that arise in the analysis and synthesis of control systems. A new numerical method based on a piecewise-quadratic (PWQ) parameterizations is developed. The proposed technique transforms a DLMI into tractable linear matrix inequalities (LMIs) that can be solved efficiently using semidefinite programming. The method is then applied to robust sampled-data control problems, yielding new tractable sufficient conditions for designing \({\mathcal {H}}_{\infty }\) H ∞ sampled-data state-feedback controllers for linear time-invariant (LTI) systems. The effectiveness of the proposed approach is demonstrated through numerical simulations, showing improved performance and reduced conservatism compared with existing methods.