Parameter-dependent and filter design for linear systems with arbitrarily time-varying parameters in polytopic domains
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摘要
In this paper, the problem of filter design for linear continuous-time systems with arbitrarily fast time-varying parameters is investigated. The time-varying parameters belong to a polytope with known vertices, affect all the system matrices and are assumed to be available online for implementation of the filters. Necessary and sufficient parameter-dependent linear matrix inequality (LMI) conditions for the existence of a parameter-dependent filter assuring that the estimation error dynamics is quadratically stable and satisfies bounds to the ve&_udi=B6V18-4RR8YMB-1&_mathId=mml30&_user=1067359&_cdi=5668&_rdoc=14&_acct=C000050221&_version=1&_userid=10&md5=813c002cfa8e2a24b5316d1f1afb7852">View the MathML source or to the ve&_udi=B6V18-4RR8YMB-1&_mathId=mml31&_user=1067359&_cdi=5668&_rdoc=14&_acct=C000050221&_version=1&_userid=10&md5=3e3824420c50600de55dc66ada29c0de">View the MathML source norms are given. A sequence of standard LMI conditions assuring the existence of homogeneous polynomially parameter-dependent (HPPD) solutions to the parameter-dependent LMIs for filter design is provided in terms of the vertices of the polytope (no gridding is required), yielding parameter-dependent filters of arbitrary degree assuring quadratic stability of the error dynamics for the ve&_udi=B6V18-4RR8YMB-1&_mathId=mml32&_user=1067359&_cdi=5668&_rdoc=14&_acct=C000050221&_version=1&_userid=10&md5=190a89d3f46ad7e118c2dafbb663f8cc">View the MathML source or the ve&_udi=B6V18-4RR8YMB-1&_mathId=mml33&_user=1067359&_cdi=5668&_rdoc=14&_acct=C000050221&_version=1&_userid=10&md5=3d71f464ecbdab808d49c2540643db57">View the MathML source cases. As the degree of the HPPD solutions increases, less and less conservative LMI conditions are obtained, tending to the necessary conditions that assure optimal values for the ve&_udi=B6V18-4RR8YMB-1&_mathId=mml34&_user=1067359&_cdi=5668&_rdoc=14&_acct=C000050221&_version=1&_userid=10&md5=7062f4323f1f73abeb01ab2e2cb36226">View the MathML source or the ve&_udi=B6V18-4RR8YMB-1&_mathId=mml35&_user=1067359&_cdi=5668&_rdoc=14&_acct=C000050221&_version=1&_userid=10&md5=ac5b3aac20389c172323594d611e5905">View the MathML source performance of the estimation error dynamics under quadratic stability. Numerical examples illustrate the results, showing that parameter-dependent filters can provide better attenuation levels than the ones obtained with robust filters, at the price of a more complex filtering strategy.

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