Published April 6, 2023 | Version v1
Conference paper Open

Cascaded Calibration of Mechatronic Systems via Bayesian Inference

  • 1. Eindhoven University of Technology
  • 2. TNO

Description

Sensors in high-precision mechatronic systems require accurate calibration, which is achieved using test beds that, in turn, require even more accurate calibration. The aim of this paper is to develop a cascaded calibration method for position sensors of mechatronic systems while taking into account the variance of the calibration model of the test bed. The developed calibration method employs Gaussian Process regression to obtain a model of the position-dependent sensor inaccuracies by combining prior knowledge of the sensor with data using Bayesian inference. Monte Carlo simulations show that the developed calibration approach leads to significantly higher calibration accuracy when compared to alternative regression techniques, especially when the number of available calibration points is limited. The results indicate that more accurate calibration of position sensors is possible with fewer resources.

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Additional details

Funding

IMOCO4.E – Intelligent Motion Control under Industry 4.E 101007311
European Commission