Collaborative Optimization Design of Event-based Asynchronous Filter for Discrete-time T-S Fuzzy Markov Jump Systems

This article is primarily concerned with the collaborative optimization design issue of the asynchronous filter and the event-based trigger for discrete-time nonlinear Markov jump systems modeled by the Takagi-Sugeno fuzzy model. In the network environment, an event-based trigger is introduced to reduce communication consumption while the hidden Markov model approach is utilized to express the asynchronous phenomenon between the filter and the plant. Based on this framework, using the parameter-dependent Lyapunov function and matrix inequality techniques, the H_∞ and L₂ − L_∞ asynchronous filter design schemes are developed such that the resulting filtering error dynamic system can be randomly mean square stable and satisfy a preset noise attenuation performance. Furthermore, an iterative optimization algorithm is presented to collaboratively solve the optimal parameters of the filter and the trigger. At last, a tunnel-diode circuit is employed to verify the validity of our design schemes.