Discrete adaptive control with multiple-step-guess estimation for brushless DC motor

Guirong Shao, Minling Zhu*, Hongbin Ma, Xinghong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The brushless DC motor (BLDCM) speed control system has various kinds of uncertainties, such as reference speed mutation, noise and parameters change, etc. However, proportional integral (PI) control method used widely cannot handle the uncertainties in the control system well. A novel discrete adaptive control with Multiple-Step-Guess (MSG) estimation for BLDCM speed control system is proposed in this contribution. MSG estimation is firstly developed and applied in BLDCM speed control system, which estimate the BLDCM model parameters online with only five steps history information sampled from the input signal and output signal. The tracking adaptive control law is designed to ensure the speed can track reference speed rapidly and accurately. Compared with PI control and recursive least square adaptive control (RLSAC), extensive simulations verify that the BLDCM speed response under MSG adaptive control (MSGAC) has better dynamic and steady state performance in the case of reference speed mutation and BLDCM parameters change. Simulation results illustrate that the novel proposed method is effective and robust for uncertainties of BLDCM speed control system.

Original languageEnglish
Pages (from-to)810-822
Number of pages13
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume23
Issue number5
DOIs
Publication statusPublished - 2019

Keywords

  • BLDCM
  • Discrete adaptive control
  • Multiple-Step-Guess
  • Parameters change
  • Reference speed mutation

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