Cooperative Decision-Making Approach for Multiobjective Finite Control Set Model Predictive Control Without Weighting Parameters

Haotian Xie, Mateja Novak, Fengxiang Wang, Tomislav Dragicevic, Jose Rodriguez, Frede Blaabjerg, Ralph Kennel, Marcelo Lobo Heldwein

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Finite control set model predictive control (FCS-MPC) has gained increasing popularity as an emerging control strategy for electrical drive systems. However, it is still a challenging task to optimize weighting parameters, as multiple objectives are involved in the customized cost function. A cooperative decision-making approach for FCS-MPC is proposed in this article, to solve the optimization problems with manifold control objectives. By splitting the cost function, the optimization problem underlying multiobjective FCS-MPC is separated into a series of decomposed optimization problems. By doing so, the dimension of the decomposed problem is reduced to one. To collect the information for decision-making, an efficient sorting algorithm is applied for each control objective. The theory behind the cooperative decision-making approach is comprehensively analyzed, to validate both the effectiveness and efficiency of the proposed scheme. More specifically, the highlight is the adaptive mechanism on the number of desired candidates, to obtain a decent performance for torque and flux. The candidate that minimizes the switching frequency is selected as the optimal. The proposed scheme is experimentally verified and compared with the existing FCS-MPC without weighting parameters.

Original languageEnglish
Pages (from-to)4495-4506
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Volume71
Issue number5
DOIs
StatePublished - 1 May 2024

Keywords

  • Cooperative decision-making
  • model predictive control
  • multiple objectives
  • weighting parameters optimization

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