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

Extracting projectile optimal fitting drag coefficient curve C/sub df/ from radar measured velocity data is considered as an optimal tracking control problem (OTCP) where C/sub df/ 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 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.