Abstract
Since aircraft control system design has experienced difficulty with unexpected aerodynamic uncertainties, estimation of these uncertainties can attenuate the performance degradation of the control system. Despite the fact that wind tunnel tests and computational analysis are used to investigate aerodynamics, model uncertainties exist in aerodynamics. At that point, we concentrate on developing an estimation method for uncertainty in the center of pressure. We build Physics-Informed Neural Networks (PINNs) with additional input variables augmented for estimation because the PINN framework can resolve inverse problems related to physics. The missile simulation time-series data with different uncertainties in the center of pressure are used in the learning of neural networks. Uncertainties in all simulation data are evaluated with estimation errors within 0.2 caliber, which are precise results. During training, the uncertainty value is updated at the same time as weights and biases in neural networks. Furthermore, sensitivity analyses are performed when the sampling time is changed and noises are added. The estimation method in this work can aid in controlling and maintaining the aerodynamic system.
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Acknowledgements
This research is supported by Agency for Defense Development and Defense Acquisition Program Administration under the Intelligence Flight Control Research Project (Contract Number: UD200045CD).
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Na, KM., Park, J., Jung, KW., Lee, CH. (2023). Estimation of Aerodynamic Uncertainty in Missile System Using Physics-Informed Neural Network Framework. In: Jo, J., et al. Robot Intelligence Technology and Applications 7. RiTA 2022. Lecture Notes in Networks and Systems, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-031-26889-2_1
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DOI: https://doi.org/10.1007/978-3-031-26889-2_1
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