Premium
Event‐triggered adaptive tracking control for a class of uncertain stochastic nonlinear systems with Markov jumping parameters
Author(s) -
He Miao,
Li Junmin
Publication year - 2018
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2936
Subject(s) - backstepping , control theory (sociology) , nonlinear system , computer science , tracking error , controller (irrigation) , markov chain , stability (learning theory) , bounded function , adaptive control , mathematics , mathematical optimization , control (management) , artificial intelligence , machine learning , mathematical analysis , physics , quantum mechanics , agronomy , biology
Summary This paper aims to investigate the problem of event‐triggered adaptive tracking control for a class of stochastic nonlinear systems with Markov jumping parameters. Since the stochastic system contains unknown parameters, the assumption of the stochastic input‐to‐state stability is a difficult task to check. To overcome the design difficulty, mode‐dependent adaptive controllers and the event‐triggered strategy are designed simultaneously with the help of backstepping technique. The stochastic input‐to‐state stability assumption is avoided by adding correction terms in controller to compensate the measurement errors. The proposed control schemes guarantee that all signals in the closed‐loop system remain bounded in probability and the tracking error signals eventually converge to the compact set in the sense of mean quartic value. Finally, simulation results show the effectiveness of the proposed approach.