Self-Calibrating Position Measurements: Applied to Imperfect Hall Sensors

TL;DR

This paper presents a data-driven calibration method for linear Hall sensors that improves rotor position accuracy without external encoders, using nonlinear identification and online compensation.

Contribution

It introduces a novel calibration procedure combining closed-loop data collection and nonlinear modeling to correct Hall sensor inaccuracies in real-time.

Findings

Measurement errors reduced to sensor noise floor in simulations

Experimental results show 2.6x reduction in RMS error

Calibration enables accurate rotor position estimation without external encoders

Abstract

Linear Hall sensors are a cost-effective alternative to optical encoders for measuring the rotor positions of actuators, with the main challenge being that they exhibit position-dependent inaccuracies resulting from manufacturing tolerances. This paper develops a data-driven calibration procedure for linear analog Hall sensors that enables accurate online estimates of the rotor angle without requiring expensive external encoders. The approach combines closed-loop data collection with nonlinear identification to obtain an accurate model of the sensor inaccuracies, which is subsequently used for online compensation. Simulation results show that when the flux density model structure is known, measurement errors are reduced to the sensor noise floor, and experiments on an industrial setup demonstrate a factor of 2.6 reduction in the root-mean-square measurement error. These results confirm that Hall sensor inaccuracies can be calibrated even when no external encoder is available, improving their practical applicability.