Quantized Feedback Control for Networked Nonlinear System with Data Dropout

This paper studies the quantized state-feedback energy-to-peak control problem for networked nonlinear system with data dropout, in which the T-S fuzzy technique is adopted to deal with nonlinear challenge and a stochastic variable satisfying the Bernoulli random binary distribution is used to model this data dropout phenomenon. A novel two-step design strategy, based on linear matrix inequality technique, is proposed for the design of controller and dynamic quantizer such that the closed-loop system being stochastically stable with the prescribed energy-to-peak performance. Finally, an application is presented to validate the effectiveness of the proposed design method.