PINNs-Based Parameter Estimation for Gradient Descent

To address the joint estimation of state and parameters for fixed-wing aircraft in wind gradient environments, this paper proposes a high-precision identification method based on a physics-informed neural network. By integrating wind gradient models with flight dynamics equations, a multi-objective optimization framework combining data-driven approaches with physical constraints is established. Employing polynomial time embedding and a dual-branch network architecture, coupled with a dynamic training strategy, simultaneous estimation of flight state and wind gradient parameters is achieved. Experimental results demonstrate that this method achieves approximately 7% parameter estimation error while accurately reconstructing unobserved attitude information. Requiring only four-dimensional observation data, this approach provides an effective solution for wind gradient glide navigation in low-cost aircraft.