Quantized L1 filtering for stochastic networked control systems based on T–S fuzzy model

Abstract

This paper is concerned with the L1 filtering problem for a class of nonlinear Ito^ stochastic networked control systems (NCSs) described by Takagi–Sugeno (T–S) fuzzy model. Considering the disadvantages of network-induced delay and quantization error in information transmission, new results on stability and L1 performance are proposed for T–S fuzzy stochastic NCSs by exploiting a Lyapunov–Krasovskii function and by means of the Ito^ stochastic differential equations. Specially, attention is focused on the design of quantization filters of both the fuzzy-rule-independent and fuzzy-rule-dependent that guarantee a prescribed L1 noise attenuation level γ with respect to all persistent and amplitude-bounded disturbance input signals. Then, when fuzzy-rule-independent filter is employed, a sufficient condition is proposed to guarantee the mean-square asymptotic stability with an L1 performance for the T–S fuzzy filtering error system. The corresponding design problem of L1 filter is converted into a convex optimization problem by solving a set of linear matrix inequalities (LMIs). Further, the parallel results with the fuzzy-rule-dependent filtering case are obtained, which have less conservatism than the fuzzy-rule-independent one. Finally, two simulation examples are provided to illustrate the feasibility and effectiveness of the proposed method.