Aerodynamic Neural Network Modeling for Gradient-based Model Predictive Flight Control

Conrad P, Steuter L, Pierer von Esch M, Beck J, Graichen K (2025)

Publication Type: Conference contribution

Publication year: 2025

Abstract

Model Predictive Control (MPC) is a promising method for flight control, offering precise stabilization and maneuvering by predicting system behavior using a model of the aircraft dynamics. Essential for these dynamics are the aerodynamic coefficients. 
While conventional aerodynamic models often do not meet the real-time requirements of flight control applications, neural networks (NN) promise to accurately capture aerodynamic behavior. However, their computational feasibility in real-time MPC remains an active research area. This paper presents a nonlinear Model Predictive Flight Control strategy for a fighter aircraft, where the numerical solution of the MPC problem requires the gradients of the aerodynamic tables. 
Instead of modeling the aerodynamic coefficients directly with NNs, we propose to use the original look-up tables and only model their derivatives with low-dimensional feedforward NNs. 
Simulation results of an MPC demonstrate enhanced computational efficiency without sacrificing accuracy, where the NN modeling makes the gradient computation more than three times faster than conventional difference quotient calculations.

Authors with CRIS profile

Paulina Conrad Chair of Automatic Control (LRT/AC) Maximilian Pierer von Esch Chair of Automatic Control (LRT/AC) Knut Graichen Chair of Automatic Control (LRT/AC)

Related research project(s)

Model predictive flight control Aug. 1, 2023 - July 31, 2026

Involved external institutions

Airbus Defence and Space GmbH DE Germany (DE)

How to cite

APA:

Conrad, P., Steuter, L., Pierer von Esch, M., Beck, J., & Graichen, K. (2025). Aerodynamic Neural Network Modeling for Gradient-based Model Predictive Flight Control. In Proceedings of the Mediterranean Conference on Control and Automation.

MLA:

Conrad, Paulina, et al. "Aerodynamic Neural Network Modeling for Gradient-based Model Predictive Flight Control." Proceedings of the Mediterranean Conference on Control and Automation 2025.

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