In general, motors have become an integral part in our daily life regardless of AC motors or DC motors. Initially, DC motors had been widely used at early stage of industrial development because of the characteristic of power source. However, field-oriented control (FOC), axis transformation techniques and three-phase inverter control have been proposed in succession, it has gradually replaced DC motors with AC motors due to the high cost and limited application range. Nowadays there are some motors that are often used in industry. It is worth noting that induction motor is one of them. An induction motor has a sturdy structure, is easy to operate, and does not require the use of magnetic materials. In some applications, it is necessary to estimate the states of the induction motor such as flux, speed and torque. This thesis compares the estimation performance and computation load using the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Firstly, the effect of various parameter tuning approaches using the UKF algorithm is examined. Secondly, simulation study of EKF and UKF based estimation is conducted with the same setting of error covariance matrix. Thirdly, the computation load using both algorithms are estimated and the impact on estimation performance is analyzed. Lastly, real-time estimation experiments are conducted under various speed and load conditions. Results show that UKF will slightly better than EKF in simulation with better induction motor model. However, in the experiment UKF results in fluctuated speed estimation due to the impact from computational load. Thus, EKF will be the better option for experiment due to less computational burden and similar estimation result.

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