Study Of Sensorless Control Scheme For PMSM Based On SMC And AFEKF

In recent years,permanent magnet synchronous motor(PMSM)has been widely used in many fields,such as servo control,aerospace,ship power,electric vehicle and industrial and agricultural production,because of its advantages of simple structure,small size,high power density,good dynamic performance and simple and reliable maintenance.It has been paid much attention by researchers in various countries.The speed and position of the motor are the core issues in controlling the motor.Traditional speed/position sensors are difficult to operate and maintain,are not suitable in harsh environments,and increase system cost.Therefore,in order to solve the above problems,the focus of this paper is PMSM position-free sensing controlFirst,the magnetic field oriented vector control id =0 is used as the basis of the permanent magnet synchronous motor.The conventional PI speed controller is poor in robustness and susceptible to external disturbances,resulting in failure to obtain satisfactory speed regulation and rotor positioning effects.As a special nonlinear control strategy,sliding mode speed controller(SMC)is widely used due to its simple controller design,fast response speed,strong robustness to parameter changes and external disturbances.In this paper,an improved sliding mode controller based on the exponential reaching law is designed for the speed overshoot of the traditional PI speed controller and the chattering of the sliding mode controlSecondly,when the traditional Extended Kalman Filter(EKF)is used to estimate the PMSM speed and position,there are problems such as inaccurate model,low estimation accuracy when the noise is uncertain,poor real-time performance,and possible filter divergence.For these problems,an adaptive fading extended Caiman filter algorithm(AFEKF)based on Sage-Husa is used.The algorithm combines the advantages of adaptive fading factor and Sage-Husa adaptive filter on the basis of EKF,which can effectively reduce the model error and improve the filtering accuracyThe results show that the performance of SMC and AFEKF is better than that of PI and EKF,which can reach the predetermined speed quickly and without overshoot at start time,and achieve a steady state quickly.The maximum deviation after loading is reduced by 1.77%than that of the traditional EKF algorithm,and the speed error of the stable state is reduced by 0.371%and the steady-state speed error is reduced by 0.45%Therefore,the SMC-based AFEKF algorithm can achieve historical forgetting of statistics,and can also cope with the effects of parameter changes and environmental noise during the operation of the PMSM,and more accurate motor speed and position estimation.