Application of neuro‐fuzzy techniques to robust speed control of PMSM

Abstract

PurposeThe purpose of the paper is to find a simple structure of speed controller robust against drive parameter variations. Application of neuro‐fuzzy technique in the controller of PI type creates proper nonlinear characteristics, which ensures controller robustness.Design/methodology/approachThe robustness of the controller is based on its nonlinear characteristic introduced by neuro‐fuzzy technique. The paper proposes a novel approach to neural controller synthesis to be performed in two stages. The first stage consists in training the neuro‐fuzzy system to form the proper shape of the control surface, which represents the nonlinear characteristic of the controller. At the second stage, the PI controller settings are adjusted by means of the random weight change procedure, which optimises the control quality index formulated in the paper. The synthesis is performed using simulation techniques and subsequently the behavior of a laboratory speed control system is validated in the experimental setup. The control algorithms of the system are performed by a microprocessor floating point DSP control system.FindingsThe proposed controller structure with proper control surface created by the neuro‐fuzzy technique guarantees expected robustness.Research limitations/implicationsThe proposed controller was tested on a single machine under well defined conditions. Further investigations are required before any industrial applications can be made.Practical implicationsThe proposed controller synthesis and its results may be very helpful in the robotic system where changing of system parameters is characteristic for many industrial robots and manipulators.Originality/valueThe original method of robust controller synthesis was proposed and validated by simulation and experimental investigations.