Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Direct Torque Control


Direct Torque Control for Permanent Magnet Synchronous Motor Based on NARMA-L2 controller

Huda B. Ahmed; Ali H. Almukhtar; Abdulrahim T. Humod

Engineering and Technology Journal, 2016, Volume 34, Issue 3, Pages 464-482

This paper investigates the improvement of the speed and torque dynamic responses of three phase Permanent Magnet Synchronous Motor (PMSM) using Direct Torque Control (DTC) technique. Different torques are applied to PMSM at different speeds during operation to ensure the robustness of the controller for wide torque variations. Optimal PI controller is used to modify the response of DTC. The optimal gains of PI controller are tuned by Particle Swarm Optimization (PSO) technique. Neural Network controller is called the Nonlinear Autoregressive-Moving Average (NARMA-L2) which is trained based on optimal PI controller (PI-PSO) data. The results show the superiority performance of using NARMA-L2 controller on PI-PSO controller for different speeds and load change. The overall simulation and design of the scheme are implemented Using MATLAB/Simulink program.

Direct Torque Control of Induction Motor Based on Neurofuzzy

Abdulrahim T. Humod; Wiam I. Jabbar

Engineering and Technology Journal, 2013, Volume 31, Issue 17, Pages 3259-3273

The main objective of this work is to improve the speed and torque responses of
three phase Induction Motor (IM) during different loads and speeds conditions.
Induction Motor is most commonly used in different industrial applications, that
require fast dynamic response and accurate control over wide speed ranges.
Therefore, this work proposes Direct Torque Control (DTC). Particle Swarm
Optimization (PSO) technique is used for optimal gains tuning of PI. The results
show the improvement in the speed response of DTC, in terms of reducing steady
state error, ripple reduction in the torque and speed responses. Neurofuzzy
(ANFIS) controller is used to improve the performance of PI-PSO controller.
ANFIS controller is trained by using PI-PSO data. The results of the ANFIS
controller are better than PI-PSO in terms of torque ripple minimization, less
steady state error in the speed response and more robustness. The simulation of the
overall drive system is performed using MATLAB/Simulink program version 7.10
(R2010a).