Auto-compensation of nonlinear influence of environmental parameters on the sensor characteristics using neural networks

• Main Authors: Patra, Jagdish Chandra, Ang, Ee Luang, Das, Amitabha, Chaudhari, Narendra Shivaji
• Other Authors: School of Computer Engineering
• Format: Article
• Language: English
• Published: 2011
• Subjects: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation
• Online Access: https://hdl.handle.net/10356/94350; http://hdl.handle.net/10220/7092
• Institution: Nanyang Technological University

Usually the environmental parameters influence the sensor characteristics in a nonlinear manner. Therefore obtaining correct readout from a sensor under varying environmental conditions is a complex problem. In this paper we propose a neural network (NN)-based interface framework to automatically compensate for the nonlinear influence of the environmental temperature and the nonlinear-response characteristics of a capacitive pressure sensor (CPS) to provide correct readout. With extensive simulation studies we have shown that the NN-based inverse model of the CPS can estimate the applied pressure with a maximum error of ± 1.0% for a wide temperature variation from 0 to 250°C. A microcontroller unit-based implementation scheme is also proposed.