Performance analysis of PMSM drive using MPSO and MOGA technique

Abstract

The major problem in permanent Magnet Synchronous Motor (PMSM) drive systems is its nonlinear behavior which arises mainly from motor dynamics and load characteristics. The presence of uncertainties further makes their control an extremely difficult task. So, the speed control strategy should be adaptive and robust for successful industrial applications. The conventional proportional integral derivative (PID) controller used to control the speed of the drive are tuned mainly using Ziegler-Nichols' (ZN) tuning technique. Since PID controller works well under the linear operating condition and underperforms when nonlinearity arises; Artificial Intelligence (AI) techniques are being implemented to achieve better performance i.e. Multi-Objective Genetic Algorithm (MOGA) and Modified Particle Swarm Optimization (MPSO). As per literature survey, not much work on MOGA & MPSO based techniques in performance analysis of PMSM drive has been reported. This paper proposes the novel design of MOGA and MPSO-AI techniques based PID speed controller which has been incorporated in PMSM drive to improve its dynamic performance. A model of PMSM drive is simulated under various operating conditions viz. Starting, load application and removal, speed reversal, to analyze its performance in terms of transient response specifications. The results obtained give the much better performance as compared to conventionally used ZN technique which is helpful in deciding the selection of said drive for a particular industrial application.