Volume 14, Issue 3 (September 2018)                   IJEEE 2018, 14(3): 278-288 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Younesi A, Tohidi S, Feyzi M R. Improved Optimization Process for Nonlinear Model Predictive Control of PMSM. IJEEE. 2018; 14 (3) :278-288
URL: http://ijeee.iust.ac.ir/article-1-1150-en.html
Abstract:   (861 Views)
Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be implemented in real time. In order to solve this problem, this paper presents an improved strategy in the field of nonlinear MPC (NMPC) of the permanent magnet synchronous motor (PMSM). The proposed method applies a sequence of reduction weighting coefficients in the cost function, over the prediction horizon. By using the proposed strategy, NMPC give a more accurate response with less number of prediction horizon. This means the computational time is reduced. It also suggests using an incremental algorithm to reduce the computational time. Performance of the proposed Nonlinear MPC (NMPC) scheme is compared with the previous NMPC methods via simulations performed by MATLAB/Simulink software, in permanent magnet synchronous motor drive system. The results show that the use of proposed structure not only lowers prediction horizon and hence computational time, but also it improves speed tracking performance and reduces electromagnetic torque ripple. In addition, using the incremental algorithm also reduces the computational time which makes it suitable for real-time applications.
Full-Text [PDF 1427 kb]   (484 Downloads)    
Type of Study: Research Paper | Subject: Adjustable Speed Drives
Received: 2017/08/16 | Accepted: 2018/02/22 | Published: 2018/02/22

Creative Commons License
© 2018 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.