Parametrizations in Control, Estimation, and Filtering Problems: Accuracy Aspects
Parametrizations in Control, Estimation, and Filtering Problems: Accuracy Aspects
Brief paper: Non-fragile H∞ filter design for linear continuous-time systems
Automatica (Journal of IFAC)
Discrete-time quantized H∞ filtering with quantizer ranges consideration
ACC'09 Proceedings of the 2009 conference on American Control Conference
Suboptimal reduced-order filtering through an LMI-based method
IEEE Transactions on Signal Processing
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Brief Non-fragile H∞ control for linear systems with multiplicative controller gain variations
Automatica (Journal of IFAC)
Technical Communique: Resilient linear filtering of uncertain systems
Automatica (Journal of IFAC)
Improved robust H2 and H∞ filtering for uncertain discrete-time systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
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Information Sciences: an International Journal
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International Journal of Automation and Computing
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This paper is concerned with the problem of designing insensitive H"~ filters for linear continuous-time systems. Coefficient sensitivity functions of transfer functions with respect to filter additive/multiplicative coefficient variations are defined, and the H"~ norms of the sensitivity functions are used to measure the sensitivity of the transfer functions with respect to additive filter coefficient variations. In addition, in order to deal with the filter design problem for the multiplicative filter coefficient variation case, new measures based on the average of the sensitivity functions are also defined. Consequently, the insensitive H"~ filter design problem is reduced to a multi-objective filter design problem, which minimizes the coefficient's sensitivity and meets the prescribed H"~ norm constraint simultaneously. First, a novel method for designing insensitive H"~ filters subjected to additive filter coefficient variations is given in terms of the linear matrix inequality (LMI) optimization techniques. Furthermore, based on the new sensitivity measures, the obtained results are extended to the multiplicative coefficient variation case. In addition, an indirect method for solving the multiplicative variations is also proposed. Finally, two numerical examples are provided to demonstrate the effectiveness of the proposed method.