Abstract
The nonlinear response characteristics of a capacitive pressure sensor (CPS) changes when the ambient temperature changes widely. In such conditions, the calibration becomes difficult, and to obtain an accurate pressure readout, appropriate compensation of the CPS characteristics is needed. We propose an intelligent CPS using artificial neural networks (ANNs) to provide self-calibration and compensation. The proposed ANN model can provide automatic nonlinear compensation and calibration of the CPS characteristics. A microcontroller unit (MCU) based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum full-scale error of /spl plusmn/1% over a variation of temperature from -50 to 150/spl deg/C.
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