Network-based fuzzy control for nonlinear Markov jump systems subject to quantization and dropout compensation

This paper focuses on the issue of network-based fuzzy control for nonlinear Markov jump systems with unreliable communication links. The nonlinear system under consideration is described by a Takagi-Sugeno (T-S) fuzzy model through corresponding fuzzy rules. The control signals are quantized by a logarithmic quantizer before they are transmitted to the network, and in case quantized control signals lose intermittently when being passed to the actuator, a compensation strategy is implemented to deal with the packet dropout. Based on a novel Lyapunov function which is both fuzzy-basis-dependent and mode-dependent, the existence criterion for the desired controller is established to ensure the stochastic stability as well as a predefined H-infinity performance index of the resulting closed-loop system. A bench mark example of robot arm is presented to demonstrate the validity of the proposed design technique. (C) 2018 Elsevier B.V. All rights reserved.