Parameter optimization of model predictive control by PSO

Ryohei Suzuki, Fukiko Kawai, Chikashi Nakazawa, Tetsuro Matsui, Eitaro Aiyoshi

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Among various control methods, model predictive control (MPC) is one of the major control strategies and has many successful applications. This paper presents an automatic tuning method for MPC using particle swarm optimization (PSO). One of the challenges in MPC is how control parameters can be tuned for various target plants, and the use of PSO for automatic tuning is one of the solutions. The MPC tuning problem is formulated as an optimization problem and PSO is applied as the optimization technique. PSO is one of the metaheuristic methods which are known to seek a global optimum at a relatively high ratio and with no use of a gradient. The numerical results for simple examples show the effectiveness of the proposed PSO-based automatic tuning method.

Original languageEnglish
Pages (from-to)40-49
Number of pages10
JournalElectrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
Issue number1
Publication statusPublished - 2012 Jan 15


  • metaheuristics
  • model predictive control
  • parameter optimization
  • particle swarm optimization

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering


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