Estimation of lateral-directional aerodynamic derivatives from flight data using conventional and neural based methods

The paper presents the estimation of lateral-directional aerodynamic derivatives (parameters) using conventional and neural based methods from real flight data of Hansa-3 aircraft. The conventional methods such as least squares (LS) and maximum likelihood (ML) require an a priori postulation of mathematical model to estimate the parameters. Whereas the neural-based method such as Neural-Gauss-Newton (NGN) is an algorithm that utilises feed forward neural network and Gauss-Newton optimisation to estimate the parameters and does not require any a priori postulation of mathematical model or solution of equations of motion. In the paper, the LS, ML and NGN methods are applied to lateral-directional flight data in order to estimate parameters. The results obtained in terms of lateral-directional aerodynamic derivatives are reasonably accurate to establish LS, ML and NGN as parameter estimation methods along with NGN method having an additional advantage of non-requirement of a priori mathematical model. The paper also highlights the effect of different types of control inputs on parameter estimation. For this, three types of control inputs were used to generate real flight data. The ailerons and rudder were deflected in the first, the ailerons were deflected while keeping rudder at trim condition in the second and the rudder was deflected while keeping ailerons at trim condition in the third type of control input to generate the real flight data. The paper presents the effect of three different types of control inputs in terms of aerodynamic parameters estimated through conventional and neural based methods using flight data generated through these inputs.