Extended Speed Current Profiling Algorithm for Low Torque Ripple SRM Using Model Predictive Control

Advanced Control of Switched Reluctance Motors (SRMs): A Review on Current Regulation, Torque Control and Vibration Suppression

“…A similar approach is presented in [69], where an unconstrained MPCC is combined with an inductance auto-calibration mechanism, resulting in a stochastic MPCC with adaptive model calibration. A MPCC making use of a high accuracy lookup table (LUT) based on a semi-numerical machine model is described in [70]. In [71] a virtual-flux FCS-MPCC for SRMs is proposed.…”

“…An initial reference current with trapezoidal shape was utilized and then tuned twice with instantaneous torque feedback to obtain the final reference current profile in [108]. The work was extended in [109] to a four quadrant operation, and further enhanced in [70] by considering the rate of current variation, improving highspeed performance. The work of [110] analytically derived the reference current based on the unsaturated SRM to not only reduce the torque ripple but also suppress the bus current ripple.…”

A torque sharing function with online compensation of excitation current control method for torque ripple reduction in electric excitation auxiliary switched reluctance motor drives

“…There are mainly several methods to suppress the torque ripple of SRM, such as direct torque control (DTC), flux control, direct instantaneous torque control, torque sharing function (TSF) control, harmonic current injection method, neural network, fuzzy control, predictive control and sliding mode control (SMC) [11][12][13][14][15]. Reference [16] presented a design of a sliding mode speed controller for DTC, which improved the speed control accuracy.…”

“…This method adjusts the three-phase reference current in real time to keep the output torque stable and improves the control accuracy, but it has higher requirements on the system hardware equipment. In addition to the above several control methods that can suppress the torque ripple of SRM, the references [14][15][16]19] also combined the neural network, fuzzy control and predictive control and SMC algorithm with control methods to obtain a more stable output torque.…”

Geometric Modification of a Switched Reluctance Motor for Minimization of Torque Ripple using Finite Element Analysis for Electric Vehicle Application

“…The torque ripple was seen to reduce from 38.33% to 16.67% by increasing the torque-ampere ratio. More recently, torque position based current modelling and model predictive control techniques have been considered in reducing torque ripple in SRM [29] [30]. M Debouza et al proposed a novel grey wolf optimization technique to optimize the SRM converter firing pulses to produce the lowest possible torque ripples [31].…”