A new insight on the event-triggered state feedback control for Markov jump systems

The event-triggered control of Markov jump systems has attracted more and more interest in field control. However, the problem of how to design a transition probability-dependent event-triggered mechanism and controller has not been fully considered. This paper investigates the problem of event-triggered control for Lipschitz nonlinear Markov jump systems. Through Taylor series expansion, a linear auxiliary system is constructed to obtain the approximate state, whose system matrices are described by the probability-weighted matrices of nonlinear Markov jump systems. By redefining the measurement error as the difference between the current state and the approximate state, a probability-dependent event-triggered mechanism is designed for Markov jump systems. The effectiveness of the developed approach is illustrated by two comparison examples.