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Event‐triggered prescribed performance adaptive fuzzy asymptotic tracking of nonstrict‐feedback nonlinear systems
Author(s) -
Hu Xiaoyan,
Li YuanXin,
Hou Zhongsheng,
Niu Ben
Publication year - 2021
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.5569
Subject(s) - control theory (sociology) , tracking error , bounded function , nonlinear system , lyapunov function , exponential stability , mathematics , boundary (topology) , uniform boundedness , tracking (education) , lyapunov stability , computer science , adaptive control , fuzzy logic , function (biology) , control (management) , artificial intelligence , psychology , mathematical analysis , pedagogy , physics , quantum mechanics , evolutionary biology , biology
This paper considers the performance‐guaranteed event‐triggered adaptive asymptotic tracking control problem for nonstrict feedback nonlinear systems with uncertain external disturbances. More precisely, the upper bounds of control gain function in stability analysis are eliminated by establishing Lyapunov function with lower bounds of control coefficients. Compared with the previous guaranteed performance results where the tracking errors converge to the neighborhood of the origin, the control scheme proposed in this paper ensures that the tracking error asymptotically converges to zero by constructing the smooth functions and boundary estimation method. So that the prescribed performance of the system is always achieved. In addition, the event‐triggered strategy is developed to reduce the waste of communication resources. The proposed control law can ensures that all the signals of system are bounded. Meanwhile, the Zeno behavior can be effectively avoid. Finally, two examples are provided to prove the effectiveness of the proposed scheme.