An ANN-based smart capacitive pressure sensor in dynamic environment
Patra, Jagdish Chandra
Bos, Adriaan Van Den
Kot, Alex Chichung
Date of Issue2000
School of Computer Engineering
A multilayer artificial neural network (ANN) is proposed for modeling of a capacitive pressure sensor (CPS). When the ambient temperature changes over a wide range, the nonlinear response characteristics of a CPS change significantly. In many practical conditions, the effect of temperature on the change in the CPS characteristics may be nonlinear. The proposed ANN model can provide correct readout of the applied pressure under such conditions. A novel scheme for estimation of the ambient temperature from the sensor characteristics itself is proposed. A second ANN is utilized to estimate the ambient temperature from the knowledge of the offset capacitance, i.e., the zero-pressure capacitance. 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 error of ±2% over a wide variation of temperature from −50°C to 150°C.
DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
Sensors and actuators A: physical
© 2000 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Sensors and Actuators A: Physical, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at DOI: http://dx.doi.org/10.1016/S0924-4247(00)00360-5.