High-order iterative learning identification of projectile's aerodynamic drag coefficient curve from radar measured velocity data

Extracting projectile’s optimal fitting drag coefficient curve Cdf from radar measured velocity data is considered as an optimal tracking control problem (OTCP) where Cdf is regarded as a virtual control function while the radar measured velocity data are taken as the desired output trajectory to be optimally tracked. With a three-degree of freedom (DOF) point mass trajectory prediction model, a high-order iterative learning identification scheme with time varying learning gains is proposed to solve this OTCP with a minimax performance index and an arbitrarily chosen initial control function. The convergence of the high-order iterative learning identification is analyzed and a guideline to choose the time varying learning gains is given. The curve identification results from a set of actual flight testing data are compared and discussed for different learning gains. These results demonstrate that the high-order iterative learning identification is effective and applicable to practical curve identification problems.