Event-Triggered Interval Estimation for Markov Jump System with Complex Transition Probabilities: A Zonotopes-Based Method

This article addresses the interval estimation problem for Markov jump systems (MJSs) with complex transition probabilities. Firstly, a dynamic event-triggered mechanism (DETM) optimizes resource use in the sensor-to-estimator communication, given the limited bandwidth in networked systems. Secondly, the stochastic stability and H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> performance of the estimation error system are analyzed. An observer is proposed using the H<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> formalism, and a zonotope-based approach is introduced to estimate system boundaries, reducing conservativeness and accurately characterizing the uncertainty region. Finally, the interval estimation of the state for the MJS is obtained, and the method's effectiveness is demonstrated through an electronic throttle model.