电动伺服机构扰动补偿与神经网络模糊控制

The fuzzy control and disturbance compensation of electric servomechanism were studied. Firstly, the system model of electric servomechanism was established according to the dynamics theory and the simulation model was built by Simulink software. Then the nonlinear disturbance such as friction moment, slot moment and time delay were fully analyzed, and a feedforward controller was designed to compensate. Secondly, in order to further improve the control performance of the system, fuzzy control was introduced to dynamically adjust the PID control parameters based on the position loop PID controller. Finally, BP neural network was used to adjust quantization factor and scale factor in real time to improve the problem of inconsistent effect on positive and negative strokes due to the asymmetry of fuzzy rules and fuzzy output domain. The control performance of traditional PID controller, fuzzy PID controller and fuzzy BP network PID controller were simulated and compared from the aspects of dynamic response ability, following performance, anti-interference ability, frequency domain response, etc. The results show that fuzzy BP neural network PID controller can improve the system response speed and improve the system control quality. It can provide reference for the design of aerospace electric servo mechanism structure and controller.