Modeling and development of an ANN-based smart pressure sensor in a dynamic environment

In many engineering applications, a capacitive pressure sensor (CPS) is placed in a dynamic environment in which the temperature variation is quite large. Since the response characteristics of a CPS are highly nonlinear and temperature dependent, in such situations, complex signal processing techniques are needed to obtain correct readout of the applied pressure. We have proposed an artificial neural network (ANN)-based smart capacitive pressure sensor, whose response characteristics can be estimated within an accuracy of ±1% error over a wide variation of temperature starting from −50°C to 150°C. This modeling scheme automatically takes care of all the nonidealities, such as, nonlinearity, offset, gain and temperature dependence, of the sensor. A novel idea of automatic collection of temperature information and its feeding into the ANN model is also proposed. In the practical implementation of this scheme, the hardware complexity poses a serious impairment. Since the tanh() functions are needed for implementation in the ANN-based model, to reduce the hardware requirement, we provide a simple scheme for computation of tanh(). Sensitivity analysis of the model with respect to the finite word-length constraint on the final stored weight values, and number of terms used in the implementation of tanh() function, have been carried out. A microcontroller-based implementation scheme for the ANN-based model is also suggested.