6533b7d1fe1ef96bd125c2d4
RESEARCH PRODUCT
Deconvolution filtering for nonlinear stochastic systems with randomly occurring sensor delays via probability-dependent method
Hamid Reza KarimiLicheng WangYuqiang LuoGuoliang Weisubject
SequenceArticle SubjectApplied Mathematicslcsh:Mathematicslcsh:QA1-939SignalNonlinear systemControl theoryBernoulli distributionConvex optimizationFiltering problemDeconvolutionVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Analyse: 411Random variableAnalysisMathematicsdescription
This paper deals with a robustH∞deconvolution filtering problem for discrete-time nonlinear stochastic systems with randomly occurring sensor delays. The delayed measurements are assumed to occur in a random way characterized by a random variable sequence following the Bernoulli distribution with time-varying probability. The purpose is to design anH∞deconvolution filter such that, for all the admissible randomly occurring sensor delays, nonlinear disturbances, and external noises, the input signal distorted by the transmission channel could be recovered to a specified extent. By utilizing the constructed Lyapunov functional relying on the time-varying probability parameters, the desired sufficient criteria are derived. The proposedH∞deconvolution filter parameters include not only the fixed gains obtained by solving a convex optimization problem but also the online measurable time-varying probability. When the time-varying sensor delays occur randomly with a time-varying probability sequence, the proposed gain-scheduled filtering algorithm is very effective. The obtained design algorithm is finally verified in the light of simulation examples.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2013-01-01 |