Research on Intelligent Algorithm of the AC Motor Speed Regulation System Based on the Neural Network

Abstract

One of the most challenging control problems has been the speed control of an alternating current (AC) motor due to the highly nonlinear characteristics and many uncertain parameters including the magnetic flux, temperature-dependent rotor resistance, and the variable load. In this study, an intelligent control algorithm is proposed for the AC motor speed control using a PID controller-based backpropagation neural network (BP-NN). The momentum factor adaptive learning rate algorithm is introduced to improve the PID-based BP-NN. A simulation model representing the complete AC motor speed regulation system is established and tested using the MATLAB program. The simulation results show that this control strategy has strong adaptability and robustness when the controlled object is unknown or the parameters change. Compared with other PID and neural network parameter adjustment methods, the model has the potential to intelligently regulate the speed of the AC motor.