Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)

Mateja Novak, Haotian Xie, Tomislav Dragicevic, Fengxiang Wang, Jose Rodriguez, Frede Blaabjerg

Research output: Contribution to journalArticlepeer-review

67 Citations (Scopus)


The use of artificial neural networks (ANNs) for the selection of weighting factors in cost function of the finite-set model-predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this article, the ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive. In the design process, the weighting factors in the cost function and the reference flux value are obtained using different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function; therefore, sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating range of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive.

Original languageEnglish
Article number9145815
Pages (from-to)7309-7319
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Issue number8
Publication statusPublished - Aug 2021


  • Artificial neural network (ANN)
  • drives
  • model-predictive torque control
  • voltage source converter (VSC)
  • weighting factor design

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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