Lateral-directional aerodynamics parameter estimation using neural partial differentiation

In this paper, application of neural networks combined with partial differentiation of the neural outputs has been discussed to estimate lateral-directional flight stability and control parameter. A neural model capable of predicting generalized force and moment coefficients using measured motion and control variables can be employed to extract aerodynamic parameters from flight data. The Neural Partial Differentiation method is used for this purpose. The estimated results are compared with the parameter estimates obtained from Output Error Method. The validity of estimates has been verified by the model validation method, wherein the estimated model response is matched with the flight-test data that are not used for estimating the parameter.