Accurate states estimation using asynchronous Kalman filter with encoder edges for TMRs

This paper proposes a new method for estimating states, such as angular velocity and position of motor system by using asynchronous Kalman filter (AKF) with quantized encoders. The AKF makes predicted states at non-periodic time to synchronize with encoder edges from quantized encoders. This method consists of periodic predictions, non-periodic predictions and updates. When encoder edges are not occurred, only periodic prediction is performed. When encoder edges are occurred, non-periodic prediction and update are performed with measuring time interval. The AKF improves accuracy of estimated states because of using additional non-periodic predictions and encoder edges values without quantization error. The performance of our method is shown by simulation.