Tolerant Sequential Model Predictive Direct Torque Control of Permanent Magnet Synchronous Machine Drives

Kai Zhang, Mingdi Fan, Yong Yang, Rong Chen, Zhongkui Zhu, Cristian Garcia, Jose Rodriguez

Research output: Contribution to journalArticlepeer-review

Abstract

Due to the need to control simultaneously torque and flux in a permanent magnet synchronous machine (PMSM), their predictive control is a multiobjective optimization problem (MOOP). To solve this problem, traditional predictive control uses the weighting factors to convert the MOOP into a single-object optimization problem. Based on the lexicographic method, a simple strategy without weighting factors called tolerant sequential model predictive control (TSMPC) is proposed. In the proposed method, the cost functions of torque and flux are, respectively, placed in two layers of the sequential structure. The voltage vectors satisfying the torque tolerance are selected as the candidates for the next layer. Then, the candidate that can minimize the flux cost function will be executed by the inverter. The advantages of the proposed TSMPC method are that it can avoid cumbersome weighting factor adjustment and directly set the tolerance value based on performance requirements. The feasibility and stability of the TSMPC strategy are validated by theoretical analysis, and the experimental results show that it has good performance.

Original languageEnglish
Article number9139410
Pages (from-to)1167-1176
Number of pages10
JournalIEEE Transactions on Transportation Electrification
Volume6
Issue number3
DOIs
Publication statusPublished - Sep 2020

Keywords

  • Model predictive control (MPC)
  • multiobjective optimization problem (MOOP)
  • permanent magnet synchronous machine~(PMSM)
  • tolerance

ASJC Scopus subject areas

  • Automotive Engineering
  • Transportation
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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