Please use this identifier to cite or link to this item: https://doi.org/10.1109/87.701354
Title: High-order iterative learning identification of projectile's aerodynamic drag coefficient curve from radar measured velocity data
Authors: Chen, Y. 
Wen, C.
Xu, J.-X. 
Sun, M.
Keywords: Aerodynamic drag coefficient
Curve identification
Data reduction
Iterative learning control
Minimax tracking
Optimal tracking control
Issue Date: 1998
Source: Chen, Y., Wen, C., Xu, J.-X., Sun, M. (1998). High-order iterative learning identification of projectile's aerodynamic drag coefficient curve from radar measured velocity data. IEEE Transactions on Control Systems Technology 6 (4) : 563-570. ScholarBank@NUS Repository. https://doi.org/10.1109/87.701354
Abstract: 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. © 1998 IEEE.
Source Title: IEEE Transactions on Control Systems Technology
URI: http://scholarbank.nus.edu.sg/handle/10635/80538
ISSN: 10636536
DOI: 10.1109/87.701354
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