Ellipse Fitting with a Dual Kalman Filter for an Adaptive Angle Tracking Observer

We present an ellipse fitting approach with a dual Kalman filter to realize an adaptive angle tracking observer (ATO), suitable for actuator motion control. In many applications where resolvers are used to sense angular displacements, the control system has high requirements for accuracy and fast dynamics. However, harsh environmental conditions and the usage of low-cost sensors impose challenges, as parameters are prone to drift, e.g. due to temperature variations or aging. The proposed ATO utilizes two estimators, one for fast state estimation and another for resolver parameter tracking. The resolvers characteristic is described geometrically with an ellipse, and we demonstrate how its parameters can be fitted online along with their uncertainty ranges. Experiments with simulated and real data from a brake-by-wire actuator show satisfying state and parameter estimation performance, which we also compare to a baseline gradient descent algorithm. The proposed adaptive ATO design, with its simple architecture built upon established state estimation tools, is attractive for various applications that use mechatronic actuators.