Conrad, Paulina (Friedrich-Alexander-Universität Erlangen-Nürnberg), Steuter, Luis (Friedrich-Alexander-Universität Erlangen-Nürnberg), Pierer von Esch, Maximilian (Friedrich-Alexander-Universität Erlangen-Nürnberg), Beck, Johannes (Airbus Defence & Space), Graichen, Knut (University Erlangen-Nürnberg (FAU))

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

Scheduled for presentation during the Regular Session "Control Applications" (WeAB), Wednesday, June 11, 2025, 10:30−10:50, Room A

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 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.