PID Adaptive Feedback Motor System Based on Neural Network
Yufang Lu, Jiehui Huang, Zhijun Jiang, Tao Tang, Haihua Tang, Lei Shi
Abstract
This paper presents a neural network-based feedback control method for enhancing the control precision and tracking speed of a permanent magnet brushless motor under command control. The proposed method involves real-time adjustment of the PID controller parameters using electromechanical output signals, enabling adaptive feedback control based on motor output. Experimental results demonstrate that this approach enhances real-time performance and dynamic load response capability, resulting in a current waveform with excellent tracking and low distortion. Overall, this method effectively improves and enhances control effectiveness. Furthermore, the developed control method is successfully applied to the development of tangible products.