Quantized Sampled-Data Stabilization for Nonlinear NCSs Subject to Successive Packet Losses and Probabilistic Sampling

This article is concerned with the stabilization problem of nonlinear networked control systems (NCSs) subject to successive packet losses (SPLs), probabilistic sampling (PS), and input dynamic quantization. Specifically, the Takagi-Sugeno (T-S) fuzzy system is used to approximate the nonlinear plant, and the model of SPLs is used to describe packet losses occurring in the sensor-to-controller and controller-to-actuator channels, while PS is represented by a Bernoulli distribution. First, by considering the double randomness of both the SPL model and PS, a unified model is proposed to evaluate the probability of the sampling interval between two successive update times. Second, by introducing probability knowledge and using the exact-time discrete approach, the stochastic stability condition is established for the constructed equivalent discrete-time T-S fuzzy system in the presence of input dynamic quantization. Third, based on the established stability condition, the fuzzy controller is designed. Finally, the networked mass-spring mechanical system is given to show the validity of the proposed approach, and the merits of the proposed design approach are discussed in comparison to existing results.