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Direct adaptive neural network control for switched reluctance motors with input saturation
Author(s) -
Li Cunhe,
Wang Guofeng,
Li Yan,
Xu Aide
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22743
Subject(s) - control theory (sociology) , switched reluctance motor , artificial neural network , constraint (computer aided design) , controller (irrigation) , computer science , lyapunov function , adaptive control , control engineering , saturation (graph theory) , electronic speed control , radial basis function , engineering , control (management) , mathematics , nonlinear system , artificial intelligence , physics , mechanical engineering , agronomy , electrical engineering , quantum mechanics , rotor (electric) , combinatorics , biology
This paper presents a novel direct adaptive neural network controller for switched reluctance motor (SRM) speed control, which takes into account parameter variations, external load disturbances, and input saturation constraint. The radial basis function (RBF) neural network based on the technology of minimal learning parameters (MLP) is employed to approximate an ideal control law that includes parameter variations and external disturbances. An auxiliary dynamic system is constructed to handle the input saturation constraint. Furthermore, uniform ultimate boundedness of all signals in the SRM drive closed‐loop control system is guaranteed through rigorous Lyapunov analysis. Comparative studies are carried out between the proposed control scheme and conventional proportional‐integral (PI) control, and the simulation and experimental results show that the proposed control scheme has better performance for parameter variations and external load disturbances. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.