Servo Drive Controller Tuning by Machine Learning Model and Particle Swarm Optimization

This research aims to provide a novel method to tune the control parameter of an unknown system in a motor drive. The study aims to achieve better control effects by adjusting the external control parameters by incorporating an additional controller. In terms of parameter adjustment methodology, a machine learning model is adopted to represent the system. The model with an accuracy of 0.95. This model is then combined with an optimization algorithm (PSO) to obtain the optimal control parameters. Compare these results with the outcomes obtained from a numerical method for motor modeling, this method improves the optimization of the characteristics by 24%.