Novel Design and Simulation of Fuzzy Controller for Turn-On & Turn-Off Angle in Coordination with SRM Speed Control for Electric Vehicles

In current scenario the Switch Reluctance Motor (SRM) are powerful alternative for Electric vehicles applications, due to its simple and rugged structure, high speed, its fault tolerance ability and Magnet free design these attributes make SRM superior to other conventional machines. This motor is a reluctance torque-driven stepper motor that can be used for bi-directional control and self-starting applications. In This paper novel control strategy proposed is to minimizing the Multiobjective function for accurate speed control of SRM by using Mamdani based two input two output fuzzy controller for optimal evaluation of α and β angle by designing closed loop system for accurate speed control of SRM and the corresponding error indices ITAE, IAE, ISE for with and without controller is analysed and compared modelling and simulation is done using MATLAB 2020a

Ant colony optimization algorithm and fuzzy logic for switched reluctance generator control

This article discusses two methods to control the output voltage of switched reluctance generators (SRGs) used in wind generator systems. To reduce the ripple of the SRG output voltage, a closed-loop voltage control technique has been designed. In the first method, a proportional-integral (PI) controller is used. The parameters of the PI controller are tuned based on the voltage variation. The SRG is generally characterized by strong nonlinearities. However, finding appropriate values for the PI controller is not an easy task. To overcome this problem and simplify the process of tuning the PI controller parameters, a solution based on the ant colony optimization algorithm (ACO) was developed. To settle the PI parameters, several cost functions are used in the implementation of the ACO algorithm. To control the SRG output voltage, a second method was developed based on the fuzzy logic controller. Unlike several previous works, the proposed methods, ACO and fuzzy logic control, are easy to implement and can solve numerous optimization problems. To check the best approach, a comparison between the two methods was performed. Finally, to show the effectiveness of this study, we present examples of simulations that entail the use of a three-phase SRG with a 12/8 structure and SIMULINK tools