Abstract: Abstract: Speed sensorless control estimates the speed of the motor by detecting the voltage and current in the motor, thereby avoiding the use of speed sensors in the system. This method can avoid the influence of the sensor on the rotation of the motor and effectively improve the speed regulation performance of the motor. The Kalman filter is widely used in speed estimation algorithm and this method is well applied to ordinary asynchronous motors, but when it is extended to bearingless induction motor(BIM), there is a mismatch, for the strong dependence of the Kalman filter on the system model, but the current mathematical model of the bearingless induction motor is an approximate model. Aiming at this problem, this study proposes a speed identification method based on improved adaptive fade-out unscented Kalman filter. By using the covariance of the residual sequence, to adaptively change the fading factor to adjust the weight of the new interest, so that the filtering algorithm is more convinced of the measured value in the estimation process, which helps to reduce the impact of stale measurement and system model uncertainty on estimation accuracy. At the same time, by normalizing the residuals, the effect of balancing the information between the residuals is achieved, and the speed of information extraction is improved. In addition, the residual part of the system model is regarded as noise. In order to further reduce the model error, the genetic algorithm is used to adaptively adjust the noise matrix. After repeated cycles of selection, crossover and mutation, the conditions for satisfying the whole population convergence can be selected. With the optimal chromosome, the noise matrix Q and R of the Manchester filter satisfying the optimal filtering condition can be obtained, and the model is compensated by the matrix, and the estimation error of the system is optimized again. In order to verify the effectiveness of the algorithm, this study selects dSPACE1005 developed by German dSPACE company as the core controller and designs the experimental platform. The platform consists of the dSPACE1005’s arithmetic control unit, computer, current sensor, voltage Hall sensor, photoelectric encoder and IPM module. The platform consists of the dSPACE1005 arithmetic control unit, computer, current sensor, voltage Hall sensor, photoelectric encoder and IPM module. The stator voltage, stator current and rotational speed are measured by current, voltage Hall and photoelectric encoder respectively. These signals are input into dSPACE1005 through signal conditioning board, and then the improved adaptive fade-out unscented Kalman filter algorithm and noise matrix are completed by dSPACE1005. The effectiveness of the proposed method is verified by comparing the estimation results of Kalman filter and the improved adaptive AFUKF. The robustness of the proposed method is verified by comparing the anti-jamming capabilities of the two algorithms. Experimental results show that this control method has high robustness and precision, and can effectively deal with the influence of modeling error and parameter disturbance on the accuracy of speed estimation. Finally, the correctness and feasibility of the proposed scheme are proved by dSPACE experimental platform.