TY - JOUR
T1 - Online Weighting Factor Optimization by Simplified Simulated Annealing for Finite Set Predictive Control
AU - Davari, S. Alireza
AU - Nekoukar, Vahab
AU - Garcia, Cristian
AU - Rodriguez, Jose
N1 - Funding Information:
Manuscript received March 3, 2019; revised September 3, 2019, November 25, 2019, and January 19, 2020; accepted February 23, 2020. Date of publication March 18, 2020; date of current version October 23, 2020. The work of C. Garcia was supported by CONI-CYT/FONDECYT Initiation Research under Project 11180235. The work of J. Rodriguez was supported by CONICYT under Project FB0008, Project ACT192013, and Project 1170167. Paper no. TII-19-0725. (Corresponding author: S. Alireza Davari.) S. Alireza Davari and Vahab Nekoukar are with the Faculty of Electrical Engineering, Shahid Rajaee Teacher Training University, Tehran 16788 15811, Iran (e-mail: [email protected]; [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2021/1
Y1 - 2021/1
N2 - Model predictive control brings many advantages and it simplifies the control scheme in power electronics. However, tuning the weighting factor is one of the important open discussions on this topic. There are online and offline methods that have been introduced to select the weighting factor. The online methods are preferred because they are more feasible. In this article, an online weighting factor optimization method based on the simulated annealing algorithm is proposed. The energy of the ripple is used as a convergence criterion. The presented method can be converged in a few steps and it does not impose cumbersome computations. Therefore, the optimal voltage will be identical for a range of the weighting factor. Furthermore, the used search algorithm is parameter independent. The proposed method is implemented for an induction motor but it is also applicable for other applications. The proposed method is validated by the experimental tests.
AB - Model predictive control brings many advantages and it simplifies the control scheme in power electronics. However, tuning the weighting factor is one of the important open discussions on this topic. There are online and offline methods that have been introduced to select the weighting factor. The online methods are preferred because they are more feasible. In this article, an online weighting factor optimization method based on the simulated annealing algorithm is proposed. The energy of the ripple is used as a convergence criterion. The presented method can be converged in a few steps and it does not impose cumbersome computations. Therefore, the optimal voltage will be identical for a range of the weighting factor. Furthermore, the used search algorithm is parameter independent. The proposed method is implemented for an induction motor but it is also applicable for other applications. The proposed method is validated by the experimental tests.
KW - Predictive control
KW - simplified simulated annealing (SA)
KW - weighting factor
UR - http://www.scopus.com/inward/record.url?scp=85091225790&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.2981039
DO - 10.1109/TII.2020.2981039
M3 - Article
AN - SCOPUS:85091225790
SN - 1551-3203
VL - 17
SP - 31
EP - 40
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 1
M1 - 9040651
ER -