Hybrid intelligent controller based torque ripple minimization in switched reluctance motor drive

Abstract

An inherent torque ripple characterizes switched reluctance technology from conventional technology. The ultimate aim of this paper is to reduce the torque ripple of the switched reluctance motor drive using genetic neural network controller based direct torque scheme. In the proposed controller network appropriate bits of data are chosen for training and testing. The proper selection of the learning rate and momentum will help in weight adjustment. Here the error is reduced which proves that the selection of voltage vectors from the vector table is precise and its results in better torque response over a wide range of speed. The simulation results reveal that the torque ripples vary between 3.25% to 1.7% for the variation in load torque and the drive speed. The experimental results for the proposed controller reveal that the torque ripple varies between 3.7% to 2.1%. Both the simulation and hardware results illustrate the efficiency of the controller.

Classical Direct Torque Control for Switched Reluctance Motor Drive

“…We call the former "flux acceleration" and the latter "flux deceleration," respectively, when the value is positive or negative. Therefore, the following constitutes a definition of a novel approach to SRM regulation [11].…”

Performance Improvement of Reluctance Synchronous Motor Using Brain Emotional Learning Based Intelligent Controller

“…The results indicate the better performance of the ANFIS controller compared to two other methods. In [30], an intelligent genetic neural network controller is proposed for minimizing a synchronous reluctance motor torque ripple. Simulation results show that, by using the proposed hybrid controller, the torque ripples vary between 3.25% and 1.7%.…”

“…In any case, the initial values for α and β in O and A, and functions R and S for generating emotional signals, must be selected. Here, the functions g and f are defined according to Equation (30) and can be defined according to the control need of the user:…”

Accounting for magnetic saturation in designing a SRM speed controller for torque ripple minimization

“…Applying this control technique to reduce torque ripple in an SRM is not allowed because to the chattering issue. Furthermore, Pushparajesh et al [24] propose a direct torque scheme based on genetic neural network controllers to decrease the switched reluctance motor torque ripple. A drawback of that method is when compared to the set performance, the calculation time is a little higher.…”