Flywheel battery for vehicle is a kind of efficient energy storage and conversion device.It is characterized by large energy storage density,quick charging speed,long service life,environmental protection and cleanliness.It can improve the dynamic performance of the vehicle in a large scale.Active Magnetic Bearing(AMB)can supply magnetic force to support the flywheel rotor.In order to improve the dynamic performance of flywheel rotor,the adaptive PID control strategy based on single neuron with varying weighted coefficient was researched in this paper.Mechanical structure and the design parameter of each link of the flywheel rotor test system were introduced,including AMBs,metal wheel hub and composite rim.By using the ANSYS software,the natural frequencies and vibration modes of flywheel rotor and the electromagnetic characteristic of AMBs were analyzed.The interference fit between magnetic bearing ring,electric motor ring,thrust plate and the rotor was calculated.The results of analysis show the validity of the design.The control system for AMBs was illustrated and the mathematical model of each link were established.The incomplete differential PID and adaptive PID control strategy based on single neuron with varying weighted coefficient were designed and the control performance with the two control strategies were investigated through MATLAB softwares.The modular development of FPGA integrated controller was completed and the control algorithm program was completed by using Verilog HDL.Finally,the static suspending and the high speed rotating experiments of flywheel rotor were realized on FPGA integrated controller.The results showed that the adaptive PID control strategy based on single neuron can ensure the flywheel rotor surpass critical speeds safely and stably.Compared with incomplete differential PID,the vibration amplitude varied smoothly over the whole speed range and the maximum vibration amplitude was less than 1.66?m under adaptive PID control strategy based on single neuron.The adaptive PID control strategy based on single neuron has stronger robustness,shorter adjust time and better dynamic performance in the face of disturbance. |