International Journal of Power Electronics and Drive System (IJPEDS) Vol. 9, No. 3, September 2018, pp. 1412~1422 ISSN: 2088-8694, DOI: 10.11591/ijpeds.v9.i3.pp1412-1422  1412 Journal homepage: http://iaescore.com/journals/index.php/IJPEDS An Adaptive Neural Network Controller Based on PSO and Gradient Descent Method for PMSM Speed Drive Zaineb Frijet, Ali Zribi, Mohamed Chtourou Control & Energy Management Lab (CEM LAB), National School of Engineering of Sfax, University of Sfax, Tunisia Article Info ABSTRACT Article history: Received Nov 24, 2017 Revised May 16, 2018 Accepted Aug 6, 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. Keyword: Adaptive neural network controller Gradient descent Particle swarm optimization Permanent magnet synchronous motor Speed controller Copyright © 2018 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Ali Zribi, Control & Energy Management Lab (CEM LAB), National School of Engineering of Sfax, University of Sfax, Tunisia. Email: ali_zribi@yahoo.fr 1. INTRODUCTION In recent years, mechanisms driven by a permanent magnet synchronous motor (PMSM) have been widely used thanks to its advantageous merits of performances, cost and reliability. However, by reason of the continuous variation of parameters, the nonlinearity of system and the inaccessibility of some states and output for measurements, improving the PMSM performances control may become challenging [1-5]. To surpass the above problems, many artificial intelligence techniques have been proposed. Neural network (NN), due to its simple conception and easy implementation, has been widely used in industrial drive applications of PMSM [6, 7]. Recently, to improve the control precision, Chao et al. proposed a new strategy of speed control for PMSM where a combination of adaptive back propagation neural network (BPNN) and PID was adopted [8]. The convergence of the proposed control scheme was proved using the Lyapunov Stability Analysis. In order to improve the robustness performance of a PMSM control, an enhanced robust fractional order proportional-plus-integral (ERFOPI) controller is presented [9]. The proposed control law is acted on a fractional order implement function (FOIF) of tracking error. Firstly, to get the parameters of the proposed controller, different tuning rules were adopted to generate the ERFOPI parameters. Secondly, to take changes of the process parameters into consideration NN was used to adjust the controller parameters. Vikas et al. in [10] proposed a NN based PID (NNPID) like controller which is tuned when the controller is operating in an on line mode for high performance PMSM position control. In the first hand, the gains of the NNPID are initialized according to a new training algorithm based on the least square solution. Then, a sequential training algorithm is used to adjust on line the controller parameters. By estimating disturbances, Reference [11] shows a simple method of improving control performance for the speed of PMSM. They