Self-Calibration of Angular Position Sensors by Signal Flow Networks

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%.

Design and Implementation of a System-on-Chip for Self-Calibration of an Angular Position Sensor

“…An experiment for another identification method is carried based on previous work [9], which processes the same error model and the operation is simple. Identification values are summarized in Table 10 and angular error is shown in Figure 18, indicated by the angle error curve after calibration with the method used in [9]. The peak-to-peak value of the error is 3.0617 • , reduced to 7.06%.…”

“…As for the analysis of execution time, the results are summarized in Table 11 for the proposed scheme and the method in [9], in which the parameter convergence time is defined as the total execution time. Table 11.…”

“…The device in the experiment is a Cyclone V FPGA with a clock frequency of 25 MHz. The algorithm in [9] is executed under the Windows 7 operating system. The programming language is python3.6 and the CPU model is i7-7700K of Intel Corporation, clocked at 4.2 GHz.…”

“…When using PrimeTime for timing analysis, the maximum frequency of the system is expected to be around 70 MHz. For the execution speed in [9], the total time of 100,000 iterations is calculated and averaged, and the time of a single iteration is 0.464 ms. From this result, the circuit execution speed is faster, but the algorithm in the circuit has low utilization of data, and the parameters are not optimal. These reasons make the total execution time in the experiment longer than that in [9].…”

“…According to this scheme, an ellipse fitting method [7] and a parameter identification algorithm [1,8] based on gradient descent were developed. On this basis, Gao [9,10] proposed iterative algorithms for self-calibration, while Wu et al [11] applied the technology detailed in [12] to design a two-step gradient estimator and realize online parameter self-calibration.…”