A Switched Reluctance Motor Drive Controller Based on an FPGA Device with a Complex PID Regulator

Abstract

This paper presents a proposal for a new type of regulator for switched reluctance motor (SRM) drives. The proposed regulator enables a significant extension of the rotational speed range and drive output power. This regulator is characterized by a complex structure, including two regulation modules: voltage and phase supply switch-on angle. The voltage module includes a proportional integral derivative (PID) voltage regulator. During its operation, the value of the phase supply switch-on angle and the width of the phase supply range are determined as a result of interpolation of the data previously determined in the simulation program. The other module contains the PID controller of the phase supply switch-on angle. The values of the angles included in the tables have been determined so as to ensure that the drive works with the greatest possible efficiency. The control method is determined based on the current operating parameters of the drive, i.e., torque and speed. The operation of the regulator was simulated in the MATLAB Simulink program. The regulator presented here was implemented in a field programmable gate array (FPGA). Tests of the regulator’s operation in the prototype system were carried out in the field of control of commutation angles.

Performance Improvement of a Switched Reluctance Motor and Drive System Designed for an Electric Motorcycle

“…A control method in the drive systems of electric motorcycles using SRMs [12] has also been previously reported. Some previous publications focused only on the performance improvement of the SRM drive system by optimizing the turn on and turn off angle at each rotating speed and vehicle load torque [13], or by simultaneously adjusting the geometry and commutation angles [14].…”

Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network

“…In studies [16][17][18][19], a variety of modern control theories, such as adaptive control, model predictive control, synovial variable structure control, and intelligent control, were applied to the speed control of the switched reluctance motor. Study [20] used an RBF neural network and a BP neural network for SRM model identification and speed control, respectively, proposing a neural network PID control strategy with strong adaptive ability and adjustable parameters. The speed regulation system built with a robust governor in study [21] effectively reduced the overshoot, rise time, and setting time of the system and achieved a faster response speed and a stronger robustness of the SRM.…”