Analysis of output signals of angular position sensors for the use of neural networks

Meier, Phil; Rohrmann, Kris; Sandner, Marvin; Prochaska, Marcus

doi:10.1109/sas.2019.8706010

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Upotrebljena je matrica senzora, zajedno sa teoretskim modelom magnetnog polja, da bi se odredila polinomijalna kompenzaciona funkcija. Istovetan princip matrice senzora je primenjen i u [12] da bi se identifikovao i kompenzovao uticaj spoljašnjeg magnetnog polja.…”

Section: Uvodunclassified

“…Jednačine (2), (10), (12), i CORDIC algoritam zaokružuju postupak merenja ugaone pozicije. Uzimajući u obzir da se inverzija matrice G može unapred spremiti, računski zahtevno je samo izračunavanje trigonometrijskih funkcija harmonijskog korektora u (12). U praktičnim realizacijama se ovo može prevazići korišćenjem tabela, ili primenom CORDIC algoritma za istovremeno izračunavanje sinusa i kosinusa ugla.…”

Section: Proces Merenja Ugaone Pozicijeunclassified

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A method for selfcalibration of a magneto-resistive angular position sensor in servo systems

Ceperkovic¹,

Drndarevic²

2020

Tehnika

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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.

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Section: Proces Merenja Ugaone Pozicijeunclassified

A method for selfcalibration of a magneto-resistive angular position sensor in servo systems

Ceperkovic¹,

Drndarevic²

2020

Tehnika

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“…In contrast to the aforementioned examples the following work extends [35] and [36] in order to correct a measurement error that is induced by homogeneous disturbing stray fields with an array of AMR angular position sensors.…”

Section: Related Workmentioning

confidence: 99%

“…Different network topologies such as convolutional neuronal networks (CNN), residual neuronal networks (RNN), or feed forward networks (FFN) are preferred depending on the designated application. Extending [35] and [36] this work examines the application of FFNs and CNNs with regard to the improvement of the stray field immunity of angular sensing applications.…”

Section: Artificial Neuronal Networkmentioning

confidence: 99%

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Compensation of Measurement Errors for a Magnetoresistive Angular Sensor Array Using Artificial Neuronal Networks

et al. 2020

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Sensing setups based on the magnetic field possess many benefits that make such sensors an important class in many application fields. Especially in industrial or automotive applications, such sensing concepts are very important parts in many essential system components. Magnetic field sensors are typically used to measure angular positions and rotational velocity as well as linear positions. Due to their high robustness and accuracy, MR-based sensors have a dominant role in the angular sensing domain. Nevertheless, novel vehicle concepts such as electrical driven or hybrid cars as well as increasing quality and reliability requirements demand improvements. However, those sensors face challenges due to new technologies, which are accompanied by new regulations. From this follows that manufactures can either adapt existing technologies or develop new sensing concepts. The goal of the following work is the evaluation of the capability of neuronal networks to enhance the measurement results of magnetic field sensors if these are disturbed by magnetic stray fields. Therefore, the system setup and possible error sources are analyzed before different neural networks are designed, tried and tested, whereby special attention is given to the network configuration in order to enable a deployment close to the individual sensors. Finally, the results are verified with an adapted measurement setup.

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Single Buffered Angular Speed Measurement Method for Self-Calibration of Magnetoresistive Sensors

Ceperkovic

Prokin

Prokin³

2023

2023 12th Mediterranean Conference on Embedded Computing (MECO)

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Analysis of output signals of angular position sensors for the use of neural networks

Cited by 7 publications

References 14 publications

A method for selfcalibration of a magneto-resistive angular position sensor in servo systems

A method for selfcalibration of a magneto-resistive angular position sensor in servo systems

Compensation of Measurement Errors for a Magnetoresistive Angular Sensor Array Using Artificial Neuronal Networks

Single Buffered Angular Speed Measurement Method for Self-Calibration of Magnetoresistive Sensors

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