Neural network guidance based on pursuit-evasion games with enhanced performance

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This paper addresses a neural network guidance based on pursuit-evasion games, and performance enhancing methods for it. Two-dimensional pursuit-evasion games solved by the gradient-based method are considered. The neural network guidance law employs the range, range rate, line-of-sight rate, and heading error as its input variables. Additional pattern selection methods and a hybrid guidance method are proposed for the sake of the interception performance enhancement. Numerical simulations are accompanied for the verification of the neural network approximation, and of the improved interception performance by the proposed methods. Moreover, all proposed guidance laws are compared with proportional navigation.
Publisher
Elsevier
Issue Date
2006
Keywords

Missile; Guidance system; Differential games; Neural networks; Feedback control

Citation

Control Engineering Practice, Vol.14, pp.735-742

ISSN
0967-0661
DOI
10.1016/j.conengprac.2005.03.001
URI
http://hdl.handle.net/10203/22236
Appears in Collection
AE-Conference Papers(학술회의논문)

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