PINNPC: Physics-Informed Neural Network Predictive Control for MAV Transition Flight

Traditional neural networks are widely used for surrogate modeling of complex dynamics. However, they often suffer from poor interpretability, high computational costs, and an inability to provide the Jacobian matrices essential for model-based control. Physics-informed neural networks (PINNs), which embed physical laws into the training process, offer a promising alternative, yet their application to micro air vehicle (MAV) dynamics remains largely unexplored. This study proposes a PINN-based p