$H_\infty$ Model Reduction in the Stochastic Framework

  • Authors:
  • Shengyuan Xu;Tongwen Chen

  • Affiliations:
  • -;-

  • Venue:
  • SIAM Journal on Control and Optimization
  • Year:
  • 2003

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Abstract

This paper investigates the problems of $H_{\infty}$ model reduction for both continuous and discrete stochastic systems. In terms of certain linear matrix inequalities (LMIs) and a coupling nonconvex rank constraint, necessary and sufficient conditions for the existence of solutions to such problems are obtained. An explicit parametrization of all reduced-order models corresponding to a feasible solution is also proposed. In particular, when a zeroth-order $H_{\infty}$ approximation is desired, conditions are obtained using LMIs only without any rank constraints, and a parametrization of all solutions is also presented. Finally, an illustrative example is provided to demonstrate the effectiveness of the proposed approach.