Abstract
Angle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra calibration instruments. In this approach, a signal flow network is firstly constructed to overcome the parametric coupling caused by the linearization model and to ensure the independence of the parameters. The model parameters are stored in the nodes of the network, and the intermediate variables are input into the optimization pipeline to overcome the local optimization problem. A deep learning algorithm is also used to improve the accuracy and speed of convergence to a global optimal solution. The results of simulations show that the proposed method can achieve a high identification accuracy with a relative parameter identification error less than 0.001‰. The practical effects were also verified by implementing the developed technique in a capacitive APS, and the experimental results demonstrate that the sensor error after signal calibration could be reduced to only 6.98%.
Highlights
Obtaining accurate angle information is crucial for many control systems [1,2,3]
Self-calibration to improve the accuracy of an angle position sensors (APSs) when the input signal is unknown is commonly performed and necessary in practical applications
The simulation and experiment results verify the validity of the proposed method
Summary
Obtaining accurate angle information is crucial for many control systems [1,2,3]. Angle position sensors (APSs) are widely used in the aerospace and automotive industries, navigation, and other fields. Sensors including resolvers [4,5] and capacitive angular position sensors (CAPSs) [6,7] modulate the angle signals and output sets of orthogonal sine and cosine signals. Due to processing error, installation error, circuit mismatch, etc., output signals may be disturbed, resulting in unexpected errors such as amplitude deviations, direct-current (DC) offsets, and phase offsets. To obtain an accurate angle signal, it is necessary to identify the signal model parameters and calibrate the output signals. The calibration yields an accurate signal based on the identified parameters
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