PID CONTROLLER FOR SPEED CONTROL OF PMSM BASED ON MAYFLY OPTIMIZATION ALGORITHM

fatimah jaber

doi:10.30572/2018/KJE/160107

Authors

Fatimah F. Jaber Assistant lecturer at Electrical Engineering Techniques Department, Engineering Technical College/Basra, Southern Technical University,Iraq https://orcid.org/0000-0003-1610-3971

DOI:

https://doi.org/10.30572/2018/KJE/160107

Keywords:

PID, PMSM, Mayfly, MATLAB, PSO

Abstract

Permanent magnet synchronous motor (PMSM) is extensively employed in AC servo drives owing to their superior torque-to-inertia ratio, power density, efficiency, and power factor compared to other motors. So, it is a crucial point to regulate the PMSM speed. Conventional proportional, integral, and differential (PID) is a simple controller and easy to implement but it is coefficients are essentially determined by experience when used in PMSM to control the speed. This invariably produces unacceptable outcomes, in addition when it comes to low-power application drives, PID controller gains typically produce adequate results but, when it comes to high-power application drives, an untuned PID does not deliver satisfactory performance. The optimization algorithm offers an effective method to produce optimal PID gains. Therefore, to optimize the PID coefficients to regulate the PMSM speed, this study suggests a mayfly optimization algorithm (MA). Recently, the MA was introduced as a new intelligent optimization method with exceptional optimization capabilities. Nuptial dancing and random flight improve the ability of the algorithm to balance its features of exploitation and exploration while assisting in its escape from local optima. This suggested approach has been verified with MATLAB, and the outcomes are compared with the standard particle swarm optimization technique (PSO) and conventional PID. The outcomes show that compared to the standard PSO or conventional PID, the PID parameters adjusted by the MA method can produce faster speed responses and less overshoot. Furthermore, the system's optimal ITAE index value, as determined by the MA technique, is smaller (0.794) as compared to other techniques 1.503 and 1.906 respectively.

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