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Neural adaptive global stability control for robot manipulators with time‐varying output constraints
Author(s) -
Fan Yongqing,
Kang Tongtong,
Wang Wenqing,
Yang Chenguang
Publication year - 2019
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.4690
Subject(s) - control theory (sociology) , nonlinear system , computer science , artificial neural network , stability (learning theory) , lyapunov function , controller (irrigation) , function (biology) , lyapunov stability , mathematics , control (management) , artificial intelligence , physics , quantum mechanics , machine learning , evolutionary biology , agronomy , biology
Summary In this paper, a novel adaptive control scheme is proposed based on radial basis function neural network (RBFNN). The considered system is deduced by the structure of RBFNN with nonzero time‐varying parameter that installed in the fore‐end and terminal of RBFNN. With this structure and the Taylor expansion of any smooth continuous nonlinear function, a universal approximation of RBFNN is addressed according to the analysis of the character of continuous homogenous function and the Euler's theorem. The approximation accuracies can be adjusted online by the nonzero time‐varying parameter in the device with the degree of continuous homogenous function, which expand the semiglobally stability to global stability over conventional neural controller design approaches. Based on the theory analysis of barrier Lyapunov function, the violation of time‐varying constraints can be subjugated without wrecked. Finally, simulation results are carried out to verify the effectiveness by the design methods.