Online Weighting Factor Optimization by Simplified Simulated Annealing for Finite Set Predictive Control

S. Alireza Davari, Vahab Nekoukar, Cristian Garcia, Jose Rodriguez

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number9040651
Pages (from-to)31-40
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume17
Issue number1
DOIs
Publication statusPublished - Jan 2021

Keywords

  • Predictive control
  • simplified simulated annealing (SA)
  • weighting factor

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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