Application of Fractional Order Sliding Mode Control for Speed Control of Permanent Magnet Synchronous Motor

Fardila M Zaihidee,Saad Mekhilef,Marizan Mubin

doi:10.1109/access.2019.2931324

Abstract

This paper investigates speed regulation of permanent magnet synchronous motor (PMSM) system based on sliding mode control (SMC). Sliding mode control has been vastly applied for speed control of PMSM. However, continuous SMC enhancement studies are executed to improve the performance of conventional SMC in terms of tracking and disturbance rejection properties as well as to reduce chattering effects. By introducing fractional calculus in the sliding mode manifold, a novel fractional order sliding mode controller is proposed for the speed loop. The proposed fractional order sliding mode speed controller is designed with a sliding surface that consists of both fractional differentiation and integration. Stability of the proposed controller is proved using Lyapunov stability theorem. The simulation and experimental results show the superiorities of the proposed method in terms of faster convergence, better tracking precision and better anti-disturbance rejection properties. In addition, chattering effect of this enhanced SMC is smaller compared to those of conventional SMC. Last but not least, a comprehensive comparison table summarizes key performance indexes of the proposed controller with respect to conventional integer order controller.

Highlights

Fractional calculus has emerged theoretically since 300 years ago, but only in recent decades has been applied practically in a wide range of science and engineering disciplines
This paper aims to further investigate the advantage of incorporating fractional calculus in sliding mode control (SMC) for controlling real systems e.g. electrical machines
Similar results were obtained in both cases where FOSMC-controlled system produced less overshoot and tracked the reference faster compared to SMC system

Summary

Introduction

Fractional calculus has emerged theoretically since 300 years ago, but only in recent decades has been applied practically in a wide range of science and engineering disciplines. It is a generalization of the traditional integer order integration and differentiation to the non-integer order. Fractional calculus theory is applied mainly in four aspects, namely in plant or system models, estimators, optimization algorithms and controllers. Researchers have grown interest in modeling their systems/plants using fractional calculus to better interpret complex phenomena, processes and system dynamics. Inherent strengths of fractional calculus in terms of long-term memory, nonlocality and weak singularity makes it preferable to be applied in optimization problems such as signal and image processing [22], [23] and complex neural network training [24]

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