To overcome the numerical problems faced by conventional methods for the parameter estimation of linear and nonlinear unstable aircraft dynamics, an approach based on neural partial differentiation is proposed. An equation for the relative standard deviation, which is equivalent to the Cramer–Rao bound in methods such as output error approach, is derived for the statistical analysis of the estimates obtained using the neural partial differentiation approach and verified through numerical simulation. The neural architecture to be used for the parameter estimation is also discussed. It is shown that the neural partial differentiation approach is better than the least-squares approach for noisy data. The longitudinal axis stability and control derivatives are obtained for simulated data, with and without noise for an unstable aircraft, using the neural partial differentiation approach and compared with the least-squares and stabilized output error methods. Moreover, the longitudinal axis stability and control derivatives estimated from actual flight data of the unstable X-31A aircraft are compared with the least-squares and stabilized output error approaches, proving the efficacy and superiority of the proposed neural approach.
Nonlinear and Linear Unstable Aircraft Parameter Estimations Using Neural Partial Differentiation
Journal of Guidance, Control, and Dynamics ; 36 , 4 ; 1162-1176
2013-05-31
15 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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