Research On Dynamic Decoupling Control Strategy Of Bearingless Induction Motor

Bearingless induction motor(BLIM)is a new type of motor which integrates the functions of magnetic bearing and induction motor.It has the advantages of simple structure,easy to increase the speed of magnetic flux,and large stiffness coefficient of magnetic suspension force.There is a complex electromagnetic coupling relationship in the bearingless induction motor,and the decoupling between the variables is the key to realize the high performance control.In order to overcome the influence of the variation of the motor parameters on the control performance of the system,this paper studies the dynamic decoupling control strategy of bearingless induction motor.The main work includes:1.The stable performance of the bearingless induction motor is decreased,which is caused by the change of motor parameters and load disturbance,considering the stator current dynamics of bearingless induction motor based on inverse system decoupling,decoupling of the four pseudo linear subsystems design sliding mode variable structure controller(SMVSC),the exponential reaching law is introduced to reduce chattering problem of a sliding mode controller,the stability analysis was carried out by Lyapunov method.The simulation results show that the proposed scheme not only has excellent dynamic decoupling performance,but also has the characteristics of fast response speed and strong robustness.2.In order to solve the problem that the inverse model of bearingless induction motor is difficult to obtain,the decoupling control strategy based on neural network identification model and inverse system theory is studied.First of all,using learning neural network modeling by neural network inverse model;then,the neural network inverse model and the original system are connected to the bearingless induction motor is decoupled into four second-order linear subsystems,finally,the PID controller is designed for each subsystem.The simulation results show that the fault tolerance of the neural network inverse system makes the bearingless induction motor system have good anti-interference ability.3.In order to solve the problem of the weakness of adaptive control ability of the PID controller,based on the decoupling of the neural network inverse system,the PID parameters are adjusted by fuzzy control,and the fuzzy adaptive PID control algorithm is studied.The simulation results show that the fuzzy adaptive PID controller not only has the characteristics of PID control,but also has the advantages of short response time and small steady-state error.