Adaptive dynamic surface full state constraints control for stochastic Markov jump systems based on event-triggered strategy

This paper aims to investigate the event-triggered adaptive dynamic surface full state constraints control for a class of stochastic nonlinear systems with Markov jumping parameters. Using the backstepping method, we propose two adaptive dynamic surface controllers with average dwell time and the event-triggered strategies simultaneously. The existed assumption of stochastic input-to-state stability (ISS) on the stochastic systems can be avoided by adding a correction terms into the controller to compensate for the measurement error. The method designed in this work can make all signals remain bounded in probability, all the states satisfy the constraints in probability and the tracking error signals eventually converge to the compact set in the sense of mean quartic value (SMQV) for closed-loop stochastic Markov jump nonlinear uncertain system. Furthermore, the designed relative threshold strategy which relies on the control signal reduces the frequency of events triggered. Finally, the validity of put forward method is shown in simulation results. (c) 2020 Elsevier Inc. All rights reserved.

Automated summary

This paper presents an event-triggered adaptive dynamic surface full state constraints control method for stochastic nonlinear systems with Markov jumping parameters. By adding correction terms to compensate for measurement errors, the assumption of stochastic input-to-state stability on the stochastic systems is avoided. The designed method ensures bounded signals, satisfied state constraints, and eventual convergence of tracking errors to a compact set.