Switching Fuzzy Filter Design for Nonlinear Time-Varying Stochastic Systems

博士 === 國立清華大學 === 電機工程學系 === 93 === The problem of state estimation for nonlinear stochastic systems subject to time-varying parameter or time-varying structure is considered. Switching multiple-modeling approach has been used to deal with linear systems with time-varying parameters or structures. O...

Full description

Bibliographic Details
Main Authors: Chang-Lan Tsai, 蔡長嵐
Other Authors: Bor-Sen Chen
Format: Others
Language:en_US
Published: 2005
Online Access:http://ndltd.ncl.edu.tw/handle/79446637656712455714
Description
Summary:博士 === 國立清華大學 === 電機工程學系 === 93 === The problem of state estimation for nonlinear stochastic systems subject to time-varying parameter or time-varying structure is considered. Switching multiple-modeling approach has been used to deal with linear systems with time-varying parameters or structures. On the other hand, Takagi-Sugeno (T-S) fuzzy modeling method is usually adopted to approximate the nonlinear time-invariant systems, but not suitable for nonlinear time-varying systems. Combining the switching multiple-modeling approach and T-S fuzzy modeling method, a fuzzy filter, realized using switching T-S fuzzy model, is proposed for the state estimation of the nonlinear time-varying stochastic systems. In order to mitigate the model approximation error and external disturbance in the systems, the proposed H2/H∞ switching fuzzy filter minimizes the upper bound of the H2-norm of estimation error system under the constraint that the H∞-norm (i.e., the worst-case effect of disturbance on estimation error) is less than a prescribed value. The conditions for the existence of such robust filter are provided in terms of linear matrix inequalities (LMIs), allowing the use of standard convex optimization procedures to solve the proposed H2/H∞ filtering problem for nonlinear time-varying systems. Finally, numerical simulations are provided to illustrate the design procedure and to confirm the performance of the proposed robust filter.