Open AccessAn Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive
Author(s) -
Ali Zribi,
Zaineb Frijet,
Mohamed Chtourou
Publication year - 2018
Publication title -
international journal of power electronics and drive systems (ijpeds)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.322
H-Index - 21
ISSN - 2088-8694
DOI - 10.11591/ijpeds.v9.i3.pp1412-1422
Subject(s) - control theory (sociology) , gradient descent , particle swarm optimization , artificial neural network , computer science , robustness (evolution) , electronic speed control , lyapunov stability , controller (irrigation) , permanent magnet synchronous motor , lyapunov function , algorithm , artificial intelligence , magnet , control (management) , engineering , nonlinear system , agronomy , biochemistry , chemistry , physics , quantum mechanics , biology , electrical engineering , gene , mechanical engineering
In this paper, based on the combination of particle swarm optimization (PSO) algorithm and neural network (NN), a new adaptive speed control method for a permanent magnet synchronous motor (PMSM) is proposed. Firstly, PSO algorithm is adopted to get the best set of weights of neural network controller (NNC) for accelerating the convergent speed and preventing the problems of trapping in local minimum. Then, to achieve high-performance speed tracking despite of the existence of varying parameters in the control system, gradient descent method is used to adjust the NNC parameters. The stability of the proposed controller is analyzed and guaranteed from Lyapunov theorem. The robustness and good dynamic performance of the proposed adaptive neural network speed control scheme are verified through computer simulations.