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Fixed‐time adaptive neural tracking control for a class of uncertain multi‐input and multi‐output nonlinear pure‐feedback systems
Author(s) -
He Cheng,
Wu Jian,
Dai Jiyang,
Zhang Zhe
Publication year - 2021
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.3205
Subject(s) - control theory (sociology) , tracking error , controller (irrigation) , nonlinear system , mimo , computer science , bounded function , adaptive control , artificial neural network , mathematics , control (management) , artificial intelligence , channel (broadcasting) , computer network , mathematical analysis , physics , quantum mechanics , agronomy , biology
Summary This study examines the fixed‐time adaptive neural network tracking control problem for a class of unknown multi‐input and multi‐output (MIMO) nonlinear pure‐feedback systems. The introduction of the radial basis function resolves uncertain problems of unknown MIMO systems. The mean value theorem is introduced to overcome the controller design problem attributed to the nonaffine structure in pure‐feedback systems. Moreover, a novel fixed‐time virtual controller and an actual controller are designed to solve the issue of previous single‐input and single‐output and MIMO systems that have no solution in the negative domain and at the origin in finite‐ and fixed‐time controls. Furthermore, a design method is proposed. The final designed controller ensures that all signals in the system are bounded. Simulation experiments show that the designed fixed‐time controller facilitates smaller tracking error of the system compared with other finite‐ or fixed‐time controllers. Furthermore, the selection of appropriate design parameters allows the tracking error to converge on a small neighborhood of the origin in a fixed time.