Identification of Uncertain Parameter in Flight Vehicle Using Physics-Informed Deep Learning
Kyung-Mi Na,
Chang-Hun Lee
Abstract:This paper presents the estimation method for uncertain parameters in flight vehicles, especially missile systems, based on physics-informed neural networks (PINNs) augmented with a novel integration-based loss. The proposed method identifies four types of structured uncertainty: burnout time, rocket motor tilt angle, location of the center of pressure, and control fin bias, which significantly affect the missile performance. In the estimation framework, as neural networks (NNs) are updated, these uncertaintie… Show more
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