Magnetoresistive angle position sensors are, beside Hall effect sensors, especially suitable for usage within servo systems due to their reliability, longevity, and resilience to unfavorable environmental conditions. The proposed distributed method for self-calibration of magnetoresistive angular position sensor uses the data collected during the highest allowed speed shaft movement for the identification of the measurement process model parameters. Data acquisition and initial data processing have been realized as a part of the control process of the servo system, whereas the identification of the model parameters is a service of an application server. The method of minimizing of the sum of algebraic distances of the sensor readings and the parametrized model is employed for the identification of parameters of linear compensation, whereas the average shaft rotation speed has been used as a high precision reference for the identification of parameters of harmonic compensation. The proposed method, in addition to a fast convergence, provides for the increase in measurement accuracy for an order of magnitude. Experimentally obtained measurement uncertainty was better than 0.5°, with the residual variance less than 0.02°, comparable to the sensor resolution.

Subband transformation coefficients are typically computed by first filtering and downsampling of an input uncompressed signal and then subsequently filtering and downsampling the result intermediate signals with a set of lowpass and highpass filters. State‐of‐the‐art subband transformers are complex, which increases consumed power. A novel method for significantly decreasing the complexity of subband transformers to a minimum is presented. This solution was implemented in a field programmable gate array, together with various methods for compression and decompression.

Magneto-resistive and Hall angular position sensors are frequently used in servo systems, due to their reliability, longevity, and tolerance to environmental conditions. In this paper, a measurement error compensation is integrated into the servo system control structure. The compensation consists of two steps: a linear compensation of the magnetic field vector measurement error, and harmonic compensation of the angular position measurement error. The selfcalibration method uses data collected during rotation with maximal speed to identify parameters of the measurement process model. Identification is executed periodically, each time when the shaft makes a full rotation. The least squares method is utilized for identification of the linear compensation parameters, while the average rotational speed is used as an internal reference for the harmonic compensation parameters identification. The method, together with a fast convergence, increases the measurement accuracy by an order of magnitude. Experimentally achieved measurement error is below 0.7°, with residual variance below 0.05°, comparable with sensor differentiability.

This paper presents a unified model of magnetoresistive angular position sensors, applicable to sensors based on the three major sensor technologies: Anisotropic Magnetoresistance, Giant Magnetoresistance, and Tunnel Magnetoresistance magnetoresistive elements. Theoretical models of the three different magnetoresistive elements, including manufacturing tolerances, have been derived first. The models are then employed to derive a realistic model of a magnetoresistive measurement bridge. In order to construct a model of a magnetoresistive magnetic field direction sensor, besides bridge models, a novel model of a free ferromagnetic layer has been developed. Finally, a unified magnetoresistive angular position sensor model has been obtained by combining the magnetic field direction sensor and a simple model of a permanent magnet. The experimental results confirmed the validity of the proposed model.