Peak-to-peak fuzzy filtering of nonlinear discrete-time systems with markov communication protocol
Résumé
This study deals with the peak-to-peak fuzzy filtering problem for a class of nonlinear discrete-time systems with analog fading channels and communication protocol, in which the nonlinear system is modeled by the Takagi-Sugeno fuzzy model. In analog fading channels, a homogeneous Markov chain is forwarded to model the random time-varying amplitude attenuation. Aiming at alleviating the utilization of energy consumption and preventing data collision/congestion in constraint networks, a Markov communication protocol is exploited to orchestrate the data transmission, in which only one sensor can get permission to release the measurement during each time interval. In virtue of the merging strategy, a new joint Markov chain is presented to incorporate the fading channels and the communication protocol. Differently, in order to eliminate the obstacle of design conservatism, a novel peak-to-peak filter design methodology is developed, whose asynchronization is described by a nonhomogeneous hidden Markov model. Under the aforementioned framework, the resulting system is stochastically stable with a desired peak-to-peak performance index. To this end, a practical example is addressed to indicate the validity and applicability of the presented peak-to-peak filter design strategy.