Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System | Zendy

Runmin Hou | Zendy; Difen Shi | Zendy; Qiang Gao | Zendy; Yuanlong Hou | Zendy

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Fuzzy Wavelet Neural Network with the Improved Levenberg–Marquardt Algorithm for the AC Servo System

Author(s) -

Runmin Hou,

Difen Shi,

Qiang Gao,

Yuanlong Hou

Publication year - 2021

Publication title -

complexity

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.447

H-Index - 61

eISSN - 1099-0526

pISSN - 1076-2787

DOI - 10.1155/2021/8086088

Subject(s) - levenberg–marquardt algorithm , particle swarm optimization , control theory (sociology) , computer science , servomechanism , robustness (evolution) , artificial neural network , nonlinear system , fuzzy logic , algorithm , neuro fuzzy , fuzzy control system , artificial intelligence , control engineering , engineering , quantum mechanics , gene , biochemistry , chemistry , physics , control (management)

In this study, a fuzzy wavelet neural network with the improved Levenberg–Marquardt algorithm (FWNN-LM) is proposed to conquer nonlinearity and uncertain disturbance problems in the AC servo system. First of all, use the particle swarm optimization algorithm based on Levenberg–Marquardt (LM) to optimize parameters in the FWNN controller. Second, the potentiality of fuzzy rules (PFR) method is developed to optimize the structure of the FWNN by error reduction ratio (ERR). Furthermore, stability of FWNN-LM is proved by the Lyapunov method. Finally, simulation and prototype test results show that this method can improve the accuracy and robustness of the system in presence of load disturbances and parameter perturbations.

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