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Adaptive prescribed performance control of nonlinear output‐feedback systems with unknown control direction
Author(s) -
Zhang JinXi,
Yang GuangHong
Publication year - 2018
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.4277
Subject(s) - control theory (sociology) , nonlinear system , tracking error , controller (irrigation) , convergence (economics) , iterative learning control , adaptive control , computer science , control (management) , fuzzy control system , tracking (education) , fuzzy logic , mathematics , artificial intelligence , physics , quantum mechanics , psychology , pedagogy , agronomy , economics , biology , economic growth
Summary This paper focuses on the output‐feedback tracking control problem for a class of nonlinear systems with both unknown nonlinearities and unknown control directions. An adaptive prescribed performance controller combined with a Nussbaum gain and a dividing line is proposed to solve the problem. Compared with the existing results, (i) both the convergence rate and the ultimate bound of the tracking error can be prescribed; (ii) no approximating structures such as neuro/fuzzy systems are used, regardless of unknown nonlinear functions; and (iii) the computational burden is alleviated in the sense that the iterative calculation of command derivatives is avoided and the number of online learning parameters is largely reduced. Simulation results are given to further illustrate the established theoretical findings.