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Finite-time filtering for non-linear stochastic systems with partially known transition jump rates

Finite-time filtering for non-linear stochastic systems with partially known transition jump rates

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This study is concerned with the problem of robust finite-time filtering for a class of non-linear Markov jump systems (MJSs) with partially known information on the transition jump rates. The non-linearities in the system are parameterised by multilayer neural networks. Our attention is focused on the design of a mode-dependent full-order H∞ filter to ensure the finite-time boundedness of the filtering error system and a prescribed H∞ attenuation level for all admissible uncertainties and approximation errors of the networks. Sufficient conditions of filtering design are developed in terms of solvability of a set of linear matrix inequalities. A tunnel diode circuit is used to show the effectiveness and potentials of the proposed techniques.

Inspec keywords: control nonlinearities; stochastic systems; filtering theory; nonlinear control systems; linear matrix inequalities; neurocontrollers; neural nets

Other keywords: admissible uncertainties; partially known transition jump rates; mode-dependent full-order H∞ filter; linear matrix inequalities; nonlinear stochastic systems; robust finite-time filtering; tunnel diode circuit; approximation errors; nonlinear Markov jump systems; filtering error system; multilayer neural networks; H∞ attenuation level

Subjects: Time-varying control systems; Linear algebra (numerical analysis); Neurocontrol; Nonlinear control systems

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