Speed control of Switched Reluctance Machine using fuzzy controller and neural network

Oubouaddi, Hafid; Brouri, Adil; Ouannou, Abdelmalek

doi:10.1109/iscv54655.2022.9806121

Cited by 4 publications

(1 citation statement)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, many techniques have been proposed in the literature for torque ripple and speed control of SRM using artificial intelligence. Accordingly, fuzzy logic and neural networks for speed control [7]. In [8], neural networks and PID are used for minimizing torque ripples in SRM.…”

Section: Introductionmentioning

confidence: 99%

Feedback Linearization technique for Speed and Torque control of Switched Reluctance Machine

Oubouaddi,

Brouri,

Ouannou

et al. 2023

E3S Web of Conf.

Self Cite

View full text Add to dashboard Cite

This paper proposes a feedback linearization controller design for torque ripple minimization and speed controller in switched reluctance motor (SRM) drive systems. A dynamic model system of equations in SRM is used to develop the proposed controller. The control strategy is formed by two loops. The first loop aims to control the machine's speed and generate the torque reference. In addition, the other loop is used to control the current to minimize torque ripple and the error between the desired speed and true speed. In order to validate our proposed control strategy, we used the MATLAB-Simulink toolbox. The proposed controller is being evaluated and compared to a conventional PI regulator to showcase its superior performance and advantages. Finally, the obtained results validate the proposed controller's robustness and efficiency in variable speed and load.

show abstract

Section: Introductionmentioning

confidence: 99%

Feedback Linearization technique for Speed and Torque control of Switched Reluctance Machine

Oubouaddi,

Brouri,

Ouannou

et al. 2023

E3S Web of Conf.

Self Cite

View full text Add to dashboard Cite

show abstract

Identification of switched reluctance machine using fuzzy model

Ouannou

Brouri

Kadi

et al. 2022

Int J Syst Assur Eng Manag

View full text Add to dashboard Cite

Development of Efficient Energy Management Strategy to Mitigate Speed and Torque Ripples in SR Motor Through Adaptive Supervisory Self-Learning Technique for Electric Vehicles

Saiteja,

Ashok,

Mason

et al. 2023

IEEE Access

View full text Add to dashboard Cite

Switched reluctance motors (SRM) are receiving extensive attention from the industry because of their simple construction, high reliability and efficiency. Antithetically, controlling SRM is challenging due to its nonlinearities and variable parameters such as speed, torque, flux, etc. In this study, a novel approach is used to control the speed and torque of SRM with various controllers. This methodology integrates model-in-loop (MIL) and hardware-in-loop (HIL) simulations to evaluate the efficiency of SRM in electric vehicles (Evs). To reduce the speed variations in the SRM, PID, intelligent, hybrid, and adaptive supervisory self-learning control approaches (ASSC) are used. The mathematical models of the aforementioned controllers were created in MATLAB/Simulink, and these control approaches are used to reduce speed, torque, and current fluctuations under various load and speed conditions. In this paper, both simulation and experimental work are used to investigate the various dynamic characteristics of the SRM in Evs. From the simulation findings, the proposed ASSC controller exhibits less overshoot (1.05%), settling time (0.02s) and risetime (0.01s) than PID, intelligent and hybrid control approaches. The proposed ASSC method combines numerical data and real-time rules knowledge with ANN to predict error responses in the SR motor. Also, it is significantly reducing the speed, torque, current and flux ripples of the SRM from low to high speed with different load conditions. Further, to verify the simulation results experimental work is conducted and similar results are measured under different load and speed conditions. From experimentation, efficiency maps are developed for PID, intelligent, hybrid, and ASSCs across the entire operating range. The maps show that the controller's maximum efficiency is, respectively, 85%, 88%, 91%, and 95%. The supervisory controller is 10% more efficient than PID, intelligent, and hybrid controllers in terms of various dynamic characteristics of SRM. From the observations, both experimental and simulation results corroborated that the recommended ASSC improves the SRM efficiency.

show abstract

Speed control of Switched Reluctance Machine using fuzzy controller and neural network

Cited by 4 publications

References 19 publications

Feedback Linearization technique for Speed and Torque control of Switched Reluctance Machine

Feedback Linearization technique for Speed and Torque control of Switched Reluctance Machine

Identification of switched reluctance machine using fuzzy model

Development of Efficient Energy Management Strategy to Mitigate Speed and Torque Ripples in SR Motor Through Adaptive Supervisory Self-Learning Technique for Electric Vehicles

Contact Info

Product

Resources

About