RBF neural network robust adaptive control of quadrotor aircraft

The paper presents a robust adaptive global control method based on radial basis function (RBF) neural network for quadrotors with model uncertainties and bounded external disturbances. The method combines the strong fitting ability of neural network control to unknown nonlinearities and the global stability of robust control, which solves the problem that neural network control is only semi-globally uniformity ultimately bounded, and achieves the double improvement of control accuracy and robustness. A robust controller that operates outside of the approximation domain and a neural network controller that operates within it make up the planned controller. A smooth switching function is introduced to achieve smooth switching between the two to ensure that all signals of the closed-loop system are globally uniform and ultimately bounded. Using the Lyapunov function and Barbalat's lemma, the stability of the nonlinear quadrotor aircraft system is strictly proved. Under model uncertainty and constrained external disturbance, simulations demonstrate that the suggested controller still maintains a good tracking performance for the reference trajectory, and the tracking error approaches zero. © 2024 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.