Research On Active Magnetic Bearing Controller Based On Sliding Mode Variable Structure

The pros and cons of the active magnetic bearing control performance is an important basis for judging the quality of the active magnetic bearing.Since the active magnetic bearing is a complex system,this greatly increases the difficulty in designing and optimizing the control system.This thesis focuses on the stability and robustness of the thrust active magnetic bearing(TAMB)controller and the five-degree-of-freedom active magnetic bearing controller.At present,there are two main problems in the research of stability and robustness of active magnetic bearing controller.The first point is that the model of the active magnetic bearing is not accurate enough,so it will directly lead to the final control effect is not ideal.The second point is that the designed active magnetic bearing controller has poor adaptability,and the control performance deteriorates or even fails when the system is subjected to external disturbance or system parameter changes.In view of the above problems,the research contents of this thesis are as follows:In the research of thrust active magnetic bearing controller,the dynamic model of thrust active magnetic bearing is first established.Secondly,for the shortcoming of poor adaptiveness of complementary sliding mode control,according to multi-input-multi-output(MIMO)recurrent Hermite neural network(RHNN)estimator,sliding mode control and complementary sliding mode control,adaptive complementary sliding mode controller with MIMO RHNN estimator and robust compensator is proposed.Finally,periodic sine and non-periodic ladder commands are used.Experiments show that the proposed adaptive complementary sliding mode controller has excellent stability and robust control characteristics for thrust active magnetic bearing systems under different reference estimates and conditions.Based on the research of thrust active magnetic bearing controller,the five-degree-of-freedom active magnetic bearing controller is studied.Firstly,based on the concept of decoupling and decentralized control,the decoupling dynamic model of the five-degree-of-freedom active magnetic bearing is obtained,and then,for the shortcomings of the integral sliding mode control(ISMC)and the ideal intelligent double integral sliding mode control(IDISMC)parameters are difficult to adjust.Intelligent double integral sliding mode controller with modified proportional-integral-derivative neural network(MPIDNN)is proposed.Finally,experiments are carried out under constant speed,constant speed plus load and high speed.The experimental results verify that the five-degree-of-freedom active magnetic bearing system with intelligent double integral sliding mode controller has good control performance and robustness.