Adaptive Backstepping Control of Six-Phase PMSM Using Functional Link Radial Basis Function Network Uncertainty Observer

Faa Jeng Lin, Shih Gang Chen, I. Fan Sun

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

An adaptive backstepping control (ABSC) using a functional link radial basis function network (FLRBFN) uncertainty observer is proposed in this study to construct a high-performance six-phase permanent magnet synchronous motor (PMSM) position servo drive system. The dynamic model of a field-oriented six-phase PMSM position servo drive is described first. Then, a backstepping control (BSC) system is designed for the tracking of the position reference. Since the lumped uncertainty of the six-phase PMSM position servo drive system is difficult to obtain in advance, it is very difficult to design an effective BSC for practical applications. Therefore, an ABSC system is designed using an adaptive law to estimate the required lumped uncertainty in the BSC system. To further increase the robustness of the six-phase PMSM position servo drive, an FLRBFN uncertainty observer is proposed to estimate the lumped uncertainty of the position servo drive. In addition, an online learning algorithm is derived using Lyapunov stability theorem to learn the parameters of the FLRBFN online. Finally, the proposed position control system is implemented in a 32-bit floating-point DSP, TMS320F28335. The effectiveness and robustness of the proposed intelligent ABSC system are verified by some experimental results.

Original languageEnglish
Pages (from-to)2255-2269
Number of pages15
JournalAsian Journal of Control
Volume19
Issue number6
DOIs
StatePublished - Nov 2017

Keywords

  • Adaptive backstepping control (ABSC)
  • Functional link radial basis function network (FLRBFN)
  • Lumped uncertainty
  • Lyapunov stability
  • Six-phase permanent magnet synchronous motor (PMSM)

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