An Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive

Zribi Ali (National School of Engineering of Sfax, University of Sfax, Tunisia)
Zaineb Frijet (National School of Engineering of Sfax, University of Sfax, Tunisia)
Mohamed Chtourou (National School of Engineering of Sfax, University of Sfax, Tunisia)

International Journal of Power Electronics and Drive Systems (IJPEDS)
Vol 9, No 3: September 2018

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.