Fuel mass estimation in aircraft tanks using neural nets

Determination of fuel mass in aircraft tanks is a nonlinear estimation problem, in which a set of noisy sensor readings is transformed into a single estimate. In the typical commercial aircraft currently in service, this model-based transformation is approximated by a sum of one-dimensional look-up tables representing contributions from individual fuel quantity probes. Significant improvements in accuracy may be achieved by replacing this decomposed approach with a multi-dimensional optimal estimator In this paper, feedforward neural nets are trained to approximate the unknown optimal estimator A simulation example demonstrates the benefits offered by the neural net technique compared with the traditional method. Issues related to certification of safety critical software containing neural net modules are also addressed.