An Ensemble Regulation Principle for Multiobjective Finite-Control-Set Model-Predictive Control of Induction Machine Drives

Haotian Xie, Wei Tian, Xiaonan Gao, Fengxiang Wang, Jose Rodriguez, Ralph Kennel

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Finite-control-set model-predictive control (FCS-MPC) has been widely investigated in the electrical drive systems, thanks to its merits of intuitive concept, straightforward implementation, and fast transient response. Owing to the flexible inclusion of constraints, a combination of weighting parameters is derived in the objective function to balance the relationship between the control targets. However, it is a challenging and time-consuming task to optimize a series of weighting parameters. To cope with this issue, this article proposes an FCS-MPC scheme with an ensemble regulation principle for the removal of all the weighting parameters. On the basis of the dimension reduction of the optimization problem, the ensemble regulation principle initially selects the suboptimal solutions for all the control targets. The optimal solution is determined according to a high consistency with the suboptimal solutions via an adaptive mechanism, which not only achieves a decent performance but also avoids a worst case for all the control criteria. The experimental implementation is conducted on a 2.2-kW induction machine platform, which verifies that the proposed scheme outperforms a group of existing weighting factorless FCS-MPC schemes at both the steady state and the transient state.

Original languageEnglish
Pages (from-to)3069-3083
Number of pages15
JournalIEEE Transactions on Power Electronics
Volume38
Issue number3
DOIs
StatePublished - 1 Mar 2023

Keywords

  • Ensemble regulation principle
  • model-predictive control
  • multiple control targets
  • weighting parameter optimization

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