Analog-Hall-Sensor-Based Position Detection Method With Temperature Compensation for Permanent-Magnet Linear Motor

Abstract

Analog Hall sensors are utilized to detect the mover position of tubular permanent-magnet synchronous linear motors because of their low cost and small size. Typical Hall-sensor-based position estimation methods rely on additional displacement sensors, such as optical grating rulers, to identify the measurement model of the Hall sensors, but this reliance increases the difficulty of implementation of these methods. Moreover, most position detection methods disregard temperature change in the motors, leading to a position estimation error. Thus, a measurement model that considers temperature change is established in this study to overcome these problems. An algorithm consisting of particle swarm optimization and a successive solving algorithm (SSA) is used to identify the measurement model using only Hall sensors. A new SSA with temperature compensation is proposed for real-time position estimation. In this method, the forgetting factor recursive least-squares algorithm is modified to update the measurement model at the present temperature to avoid model mismatch. The proposed method is validated using a self-designed experimental platform. The experimental results show that the model’s identification accuracy is equivalent to that of the fast Fourier transform algorithm using an optical grating ruler. The position estimation error is less than $120~\mu \text{m}$ under different operating conditions.