Neuroadaptive Dynamic Surface Asymptotic Tracking Control of a VTOL Aircraft with Unknown Dynamics and External Disturbances

This work studies the asymptotic tracking control problem of a vertical take-off and landing (VTOL) aircraft with unknown dynamics external disturbances. The nonlinear VTOL are approximated via introduction radial basis function neural networks. Then, weight update laws designed. Furthermore, parameter presented to deal errors generated during approximation process disturbances system. Moreover, first-order filters introduced avoid repeated differentiation designed virtual laws, thereby effectively eliminating “complexity explosion” caused by traditional backstepping control. Based on application network method, dynamic surface technique, neuroadaptive for system finally proposed. Theoretical analysis shows that proposed law can ensure asymptotically tracks reference trajectories converge small neighborhood zero choosing appropriate parameters. Finally, simulation examples provided verify effectiveness laws.