Study On Speed Sensorless Control Of Bearingless Induction Motor

The bearingless induction motor is a type of motor in which a coil for generating a radial levitation force is added to a stator slot,Rotation and stable suspension of the rotor of the motor can be achieved with the torque and radial force generated by the two sets of coils.BIM has many advantages that traditional motors do not have,for this reason,It is very suitable for applications in high-precision,high-speed and high-tech fields,with extremely high research and application value.BIM is a complex,multi-variable,nonlinear,and strongly coupled system so that the system is very complex.During the operation of the motor,the rotor speed needs to be detected in real time as a phase signal to complete the closed-loop control of the motor.But Since the rotor is suspended,mounting the sensor on the shaft not only increases the hardware cost,but also seriously affects the running performance of the motor.Therefore,speed sensorless control is an important issue in the research of bearingless induction motors.Supported by the National Natural Science Foundation of China(51675244),this paper conducts theoretical and experimental researches on the control of bearingless induction motors.This paper takes a two-degree-of freedom bearingless induction motor as the research object,the structure of torque winding and suspension winding is analyzed,and then the suspension principle of the BIM is studied.With the aid of Maxwell stress tensor method,the mathematical model and motion equation of two degree of freedom BIM are established,which lays a theoretical foundation for the following simulation studies.Based on traditional Kalman filter speed sensorless control,a novel extended kalman filter with a series structure is proposed.The on-line calculation of motor parameters is achieved by extending the easily changing motor parameters to the system model to be the state vector to be identified.The accurate identification of the motor speed can be realized by feeding back the obtained parameter value into the algorithm,which is conductive to reduce the impact of motor parameter variation on speed estimation accuracy.The order of system model matrix can be decreased by employing the series structure extended kalman filters,and also the computational load and complexity of digital chips in practical applications would be reduced.The comparision of estimated speed error between the traditional extended kalman filter and the novel extended kalman filter in the case of motor parameters are changed is completed by simulation and experiment.The results demonstrate that the novel kalman filter can effectively reduce the impact of parameter variation on the estimation accuracy.Finally,a BIM hardware control system based on TMS320F2812 digital controller is designed,including main circuit,control circuit and protection circuit.On the basis of the hardware circuit,the speed sensorless control was realized through the software program,and the validity of the method was finally verified...