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Almost fast finite‐time adaptive tracking control for a class of full‐state constrained pure‐feedback nonlinear systems
Author(s) -
Liu Jidong,
Niu Ben,
Zhao Ping,
Li Xiaodi,
Qi Wenhai
Publication year - 2020
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.5189
Subject(s) - backstepping , control theory (sociology) , nonlinear system , tracking error , bounded function , state (computer science) , tracking (education) , lyapunov stability , computer science , stability (learning theory) , adaptive control , lyapunov function , scheme (mathematics) , mathematics , control (management) , algorithm , artificial intelligence , psychology , mathematical analysis , pedagogy , physics , quantum mechanics , machine learning
Summary In this article, a novel almost fast finite‐time adaptive tracking control scheme is proposed for a class of full‐state constrained pure‐feedback nonlinear systems based on barrier Lyapunov functions (BLFs). First, by employing the mean value theorem, the pure‐feedback systems are converted to the strict‐feedback structure with nonaffine terms. Then, by fusing adaptive backstepping technique and BLFs, the design difficulties caused by the nonaffine terms and full‐state constraints are overcome. Furthermore, according to the predeveloped almost fast finite‐time stability criterion, it is proved that the tracking error can converge to a small compact set and all signals of the closed‐loop system can be bounded in an almost fast finite time. Finally, a simulation example of a single‐link robot is presented to verify the effectiveness of the proposed control scheme.