Intelligent Selection of Calibration Points Using a Modified Progressive Polynomial Method

Measurement errors such as gain, offset, nonlinearity, hysteresis, and cross-sensitivity degrade sensor performance, meaning that self-compensation becomes an important aspect in the maintenance of smart sensors. The progressive polynomial calibration (PPC) method is a step-by-step compensation algorithm that can fix some of the aforementioned errors by using microprocessors. In this paper, a modified-PPC (M-PPC) method is introduced. It is then shown that the number of calibration points, their selection, and permutation mechanisms can affect the M-PPC method. Therefore, an intelligent algorithm is presented to select appropriate calibration points from the input-output data. Intelligent selection of calibration points during the M-PPC method makes the calibration process simple, accurate, less time consuming, and low on computational load. A numerical example is provided to show the advantages of the proposed method in comparison with previously published ones. Finally, a test bench based on a thermistor with a nonlinearity characteristic is employed to examine the experimental results of the proposed method.