Fast Solver for Implicit Continuous Set Model Predictive Control of Electric Drives

Andrea Favato, Paolo Gherardo Carlet, Francesco Toso, Riccardo Torchio, Ludovico Ortombina, Mattia Bruschetta, Ruggero Carli, Piergiorgio Alotto, Silverio Bolognani, Jose Rodriguez

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


This paper proposes a fast and accurate solver for implicit Continuous Set Model Predictive Control for the current control loop of synchronous motor drives with input constraints, allowing for reaching the maximum voltage feasible set. The related control problem requires an iterative solver to find the optimal solution. The real-time certification of the algorithm is of paramount importance to move the technology toward industrial-scale applications. A relevant feature of the proposed solver is that the total number of operations can be computed in the worst-case scenario. Thus, the maximum computational time is known a priori. The solver is deeply illustrated, showing its feasibility for real-time applications in the microseconds range by means of experimental tests. The proposed method outperforms general-purpose algorithms in terms of computation time, while keeping the same accuracy.

Original languageEnglish
Pages (from-to)17430-17440
Number of pages11
JournalIEEE Access
Publication statusPublished - 2022


  • Electric drives
  • model predictive control (MPC)
  • permanent magnet synchronous motor (PMSM)
  • quadratic programming (QP)
  • synchronous reluctance motor (SyRM)

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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