Speed ​​Sensorless Direct Torque Control Based On Extended Kalman Method

Direct torque control technology has superior control performance, the technology has some advantages such as simple control, good dynamic characteristics and strong robustness and easy digital control etc, and has been widely used.Based on the detailed analysis of the asynchronous motor model and on the basis of further study of the Direct torque control technique, the simulation model of direct torque control system is built under Matlab/simulink simulation platform, and the control system is simulated in the presence of noise and no noise condition. The results of simulation verify the superiority of direct torque control method, at the same time, the paper summarizes some problems existing in the direct torque control system by analyzing the simulation results.In order to minimize the ripples and to improve the inaccurate speed at low speed in direct torque control system, a novel state estimation algorithm, which is based on the Extended Kalman Filter, is proposed for Sensorless control of induction motor. The application of Extended Kalman Filter in asynchronous motor is studied in this paper, and a Extended Kalman Filter observer is designed to estimate the speed and torque. Finally a direct torque control system based on the Extended Kalman Filter is constructed. Simulation results show that the robustness of drive system is enhanced by the proposed algorithm. The results also show that the system has achieved a significant improvement on the performance of the low-frequency ripple suppression and follow-up control compared with traditional direct torque control system.But, the filter performance heavily dependent on the choice of the covariance matrices, the traditional manual tuning of the Extended Kalman Filter using the trial-and-error method is simple to carry out, but the process is very time consuming and satisfactory performance can only be obtained with great effort from an experienced operator.Therefore, aiming at the problem of getting the extended Kalman filter optimal value of noise matrix using in induction motor states estimation, a novel global optimized noise matrix method, which is based on the improved particle swarm optimization algorithms, is proposed. It adopted another updated Strategies in standard particle swarm optimization. Based on improved particle swarm optimization, a global optimized nose covariance estimation method applied to extended Kalman filter in direct torque control system. Simulation results show that the proposed method can effectively improve the performance compare to those obtained by trial and error, particle swarm optimization and adaptive particle swarm optimization.