Sliding mode control and sampling rate strategy for Networked control systems with packet disordering via Markov chain prediction

This paper investigates sliding mode control combined with sampling rate control for networked control systems subject to packet disordering via Markov chain prediction. The main objectives of the proposed method are to predict the probability of the occurrence of packet disordering when packet disordering is unknown in the networks, to control sampling rate to restrain heavy packet disordering, and to stabilize the Markovian jump system with variable parameters by sliding mode techniques. Firstly, an argument system with sampling rate and a plant state is established. Then, the Networked control system based on Markov chain probability prediction and statistical analysis of this probability is modeled as a Markovian jump system with two Markov chains. Next, sliding mode controller is designed to stabilize the dynamic Markovian jump system. Finally, experiments are conducted to illustrate the effectiveness and benefits of proposed method.