Motor automation speed regulation method with sliding mode control and adaptive gain

Introduction Motor speed control is crucial for maintaining the normal operation of motors. In view of the limitations of current motor speed control methods, such as high parameter dependence, obvious control signal buffering, and low flexibility, an automatic speed control model combining Sliding Mode Control and adaptive gain is proposed. Methods This model combines adaptive gain with a Sliding Mode Control to design an Adaptive Sliding Mode Control for motor speed control. Then, the super helix algorithm is used to adjust the sliding mode gain coefficient to suppress the controller’s buffing problem. At the same time, an evaporation constant is introduced to improve the particle swarm optimization algorithm, and the controller parameters are optimized using the improved particle swarm algorithm to enhance the model’s stability and achieve automatic speed regulation of the motor. Results In the dynamic experiment, it was proposed that the current fluctuation of the model was always kept within ±0.10A, demonstrating high stability. In addition, the research proposes that the maximum speed estimation error of the model is 5.77%, which is superior to the error calculation results of the comparison models and far less than the standard requirement of 8.00%. Discussion The model proposed in this study exhibits superior speed regulation performance, achieving high stability, low vibration, and strong robustness in motor automatic speed regulation control. It can better meet the speed control requirements in the field of motors, thereby better ensuring the safe operation of the motor.