Paper Info
Title
Adaptive Dynamic Surface Full State Constraints Control For Stochastic Markov Jump Systems Based On Event-Triggered Strategy
Abstract
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.
Year
DOI
Venue
2021
10.1016/j.amc.2020.125563
APPLIED MATHEMATICS AND COMPUTATION
Keywords
DocType
Volume
Stochastic nonlinear systems, Event-triggered control, Dynamic surface control, Full state constraints
Journal
392
ISSN
Citations 
PageRank 
0096-3003
1
0.35
References 
Authors
0
4
Name
Order
Citations
PageRank
Miao He111.02
Taotao Rong210.35
Jun-Min LI339036.09
Chao He410.35