Auto-Tuning Technique for the Cost Function Weight Factors in Model Predictive Control for Power Electronics Interfaces

Mohammad B. Shadmand, Sarthak Jain, Robert Balog

Research output: Contribution to journalArticle

4 Citations (Scopus)


This paper presents an auto-tuning technique for online selection of the cost function weight factors in model predictive control (MPC). The weight factors in the cost function with multiple control objectives directly affect the performance and robustness of the MPC. The proposed method in this paper determines the optimum weight factors of the cost function for each sampling time; the optimization of the weight factors is done based on the prediction of the absolute tracking error of the control objectives and the corresponding constraints. The proposed method eliminates the need of trial-and-error approach to determine a fix weight factor in the cost function. The application considered is a capacitor-less static synchronous compensator (STATCOM) based on MPC of a direct matrix converter (DMC). This technique compensates lagging power factor loads using inductive energy storage elements instead of electrolytic capacitors (e-caps). The result demonstrates that the proposed auto-tuning approach of cost function weights makes the control algorithm robust to parameter variation and other uncertainties in the system. The proposed capacitor-less reactive power compensator based on auto-tuned MPC cost function weight factor is verified experimentally.

Original languageEnglish
JournalIEEE Journal of Emerging and Selected Topics in Power Electronics
Publication statusAccepted/In press - 26 Jul 2018



  • Auto-tuned weight factors
  • Automatic voltage control
  • Capacitor-less STATCOM
  • Capacitors
  • Cost function
  • Matrix converters
  • model predictive control (MPC)
  • Reactive power
  • reactive power compensation
  • Robustness

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering

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