Control Unit Design Utilizing ESC for Switched Reluctance Motor

Abstract

Switched Reluctance Motors (SRMs) have a basic design, robustness, and fault-tolerant nature, offering scope for their extensive use in industrial and automotive applications. Still, their performance is hampered by significant disadvantages, including acoustic noise and high torque ripple, whose solutions often involve expensive driver circuits. The goal of this project is to design and create a controller for a Switched Reluctance Motor with the intention of minimising cost while maintaining acceptable torque ripple and efficiency levels. The suggested controller utilises an Arduino Nano, implementing extreme seeking optimisation for ideal turn-on and turn-off angles, as well as appropriate phase excitation control. Compared to traditional control techniques, the developed controller offers acceptable output voltage pulses and other characteristics within the acceptable range for the operation. Effective controller design improves SRM cost-effectiveness considerably while also keeping the design simple for easy scalability.

Introduction

Switched Reluctance Motors (SRMs) are magnet-free machines known for simple construction, low cost, and robustness, making them suitable for industrial use and electric vehicles, especially where rare-earth magnet dependence is undesirable. However, their main drawback is high torque ripple caused by nonlinear magnetic behavior and discrete phase switching, which leads to vibration, noise, and reduced efficiency. This makes advanced control strategies essential.

The project proposes a low-cost SRM controller using an Arduino Nano, an asymmetrical bridge converter, and Extremum Seeking Optimization (ESO). The controller dynamically adjusts turn-on and turn-off angles to reduce torque ripple while maintaining efficiency. The asymmetrical bridge enables independent phase control with efficient switching modes, while ESO continuously searches for optimal firing angles without requiring gradient information. Data logging via PLX-DAQ enables performance analysis in Excel.

Since a physical motor was not used, SRM behavior was simulated with RL loads and stochastic speed modeling. The controller includes closed-loop phase commutation, simulated speed ramping up to 1500 RPM with noise, and real-time optimization starting after a threshold speed. The ESO algorithm gradually shifts the firing angle from 15° toward an optimal value around 24°, improving torque smoothness.

Results show that optimized control reduces torque ripple, stabilizes torque output, and improves speed regulation while maintaining expected SRM torque-speed characteristics. Voltage waveforms confirm correct phase switching, and torque data shows clear stabilization after optimization. Overall, the system demonstrates that a simple microcontroller-based ESO controller can effectively improve SRM performance with reduced cost and computational complexity, though challenges remain in scaling, real-world implementation, and further refinement of control accuracy.

Conclusion

A controller for a Switched Reluctance Motor was created and successfully implemented in this project to obtain acceptable controller which produced torque ripple of about 20.04% while keeping the overall cost of the machine to be as low as possible. The 20 % is considered a standard value for torque ripple.[17] The suggested control approach enables effective control of phase currents and excitation angles, hence provide smoother torque output. The controller\'s success is confirmed by experimentalfindings which are in accordance with the theoretical and prior research. Through the appropriate control methods, SRMs can be rendered fit for high-performance applications, as shown by the developed system.Further performance improvement may include the implementation of sophisticated control systems, such as neural networks or fuzzy logic, in the next research which are found to reduce the torque ripple to sub 10%-20% .[18,19,20]

References

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Copyright

Copyright © 2026 Deepak Saini, Manvish Ghusinga, Lakshya Kumar Jindal, Pulkit Dhangar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.