Batch-triggered identification based adaptive tracking control for fixed-wing UAV

In this paper, an adaptive hierarchical control method based on a regulation-triggered batch identifier is proposed. This approach addresses the challenges in stability analysis and control design of fixed-wing UAVs, which are complicated by the coupling of underactuated characteristics and parametric uncertainty. In order to deal with the underactuated problem, the original fixed-wing UAV model is transformed into a virtual integrator and linear parameterized model based on the unicycle model. Backstepping is then combined with fixed-time control methods to create an equivalent flight tracking controller. By analyzing the mechanism of parameter uncertainty on the motion state of the UAV, a linear regression equation is constructed by spectral decomposition technology to quantitatively collect excitation information, which ensures the richness of the collected excitation information and the boundedness of the linear regression equation. A regulation-triggered batch identifier is created to update the parameter estimation using the gathered excitation data when the trigger function satisfies specific requirements in order to remove the impact of parametric uncertainty. Compared with the existing hierarchical control strategies for fixed-wing UAVs, the proposed framework achieves the unity of error convergence, excitement richness and parameter identification accuracy.