Abstract
Measuring temperature and moisture are important in many scenarios. It has been verified that temperature greatly affects the accuracy of moisture sensing. Moisture sensing performance would suffer without temperature calibrations. This paper introduces a nonlinearity compensation technique for temperature-dependent nonlinearity calibration of moisture sensors, which is based on an adaptive nonlinear order regulating model. An adaptive algorithm is designed to automatically find the optimal order number, which was subsequently applied in a nonlinear mathematical model to compensate for the temperature effects and improve the moisture measurement accuracy. The integrated temperature and moisture sensor with the proposed adaptive nonlinear order regulating nonlinearity compensation technique is found to be more effective and yield better sensing performance.
Highlights
Temperature and moisture sensors have extensive applications in many industries, including agriculture, food, pharmaceuticals, mining, construction, and so on
It is obvious that the measurement error is very large without correction, which shows that temperature has a significant and nonlinear influence on the measurement results
For the sensors studied in this article, the moisture error ratio reached a minimum under the eighth-order nonlinear compensation model, which indicated the best correction effect
Summary
Temperature and moisture sensors have extensive applications in many industries, including agriculture, food, pharmaceuticals, mining, construction, and so on. Manufacturers perform huge numbers of tests under various temperatures and moisture levels in order to construct LUTs. the accuracy of this nonlinear mapping relationship depends on the precision of the measurements and the size of the LUT [7,8]. A mathematical model of nonlinear adjustment can overcome the shortcomings of conventional sensors, and to a large extent, reflect and compensate the complex effect of temperature on moisture readings [9,10,11]. The existing compensation methods often use nonlinear mathematical models with fixed orders, which may not reach optimal error under different usage environments, making it difficult to attain the best compensation effects. The experimental results verify that the measurement accuracy of this adaptive order nonlinearity compensated temperature and moisture sensor is significantly improved
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