Composite Learning Control for UAVs via Prescribed Performance

Unmanned quadrotors have been widely used in numerous practical application scenes, and attracted a great interest in control community. Our work is to achieve finite-time trajectory tracking of quadrotors under perturbations. Finite-time control is achieved by prescribed performance technique. A new prescribed performance function, which owns finite-time convergence property, is defined. This scheme produces less-complex control design for finite-time control and possesses the advantages of prescribed performance control. Furthermore, the composite learning, which combines nonlinear disturbance observer and direct adaptive neural control, is applied to improve the approximation performance and enhance system robustness. In view of the cascade structure of quadrotor dynamics, command-filter-based backstepping framework is adopted, where the proposed control techniques are integrated into. Finally, several comparative simulations demonstrate the effectiveness and superiority of the proposed method.