Asynchronous adaptive quantized feedback sliding mode control for semi-markovian jump systems: An event-triggered approach

Abstract

This paper is concerned with the problem of event-triggered based asynchronous adaptive quantized sliding mode control (SMC) for Itoˆ stochastic control systems with semi-markovian switching and actuator failures. In this design work, the jump modes between plants and controller are asynchronous, which is represented by a hidden Markov model. The dynamical logarithmic quantizer is introduced in signal quantization both in sensors-to-controller and controller-to-actuators sides, and a new event-triggered detector condition is developed based on the quantized state vector. Combined with SMC and adaptive control techniques, a new event-triggered based asynchronous adaptive quantized SMC law is designed to stabilize the Itoˆ stochastic systems with semi-markovian switching, actuator failures and signal quantization. The proposed control law has ability to guarantee the robust performance of the closed-loop system in the presence of the effects of actuator failures, time delay resulting by event-triggered and signal quantization error. Moreover, the developed event-triggered based asynchronous adaptive quantized feedback SMC scheme can ensure that the sliding surface converges the defined small domain in finite time. The lower bound for inter-execution time is analyzed and presented, and sufficient condition under which the zenor behaviors can be avoided is given. Finally, an example is provided to demonstrate the effectiveness of the proposed design techniques.