Physics-Informed Machine Learning for UAV Control

The integration of Dynamic Mode Decomposition (DMD) control with Physics-Informed Neural Networks (PINNs) offers a novel approach to enhancing UAV quadcopter control systems. This method leverages DMD techniques and PINNs to solve the Riccati equation, which is essential for precise UAV position estimation. By framing the UAV control problem within physics-based constraints, the approach ensures that the learned models adhere to the physical laws governing UAV dynamics. Utilizing a comprehensive dataset-including UAV flight parameters such as position, velocity, and control inputs-DMD extracts fundamental dynamic modes from the data, providing a reduced-order representation that captures the dominant UAV dynamics. This reduced representation is then incorporated into the PINN framework to accurately solve the Riccati equation. The combination of DMD and PINNs results in a robust control strategy that significantly improves position estimation accuracy and optimizes control performance. Real-time validation of this method was conducted in a Unity-based physics simulation environment, incorporating factors like gravity and perturbation noise. The results demonstrate substantial improvements in estimation accuracy and control stability compared to traditional methods.