Adaptive Neural Control for a Tilting Quadcopter with Finite-Time Convergence

This paper addresses an Adaptive Neural Control (ANC) with fast finite-time convergence for a tilting quadcopter with parametric uncertainties. We propose a novel concept, which suggests the translational and rotational movements can be controlled independently. Namely, a full controllability with 6 degrees of freedom (DOF) is achieved. The complete dynamics based on Euler-Lagrange is developed. To compensate uncertainties in system, a new adaptive scheme is incorporated into ANC, where estimation errors can be abstracted and used as a new term in the adaptive scheme. All tracking and estimation errors can converge to a small neighborhood around zero in finite-time proved by Lyapunov and Finite-time theories. Moreover, the estimated weights of Neural Networks (NNs) can be guaranteed to converge to their optimal values simultaneously. Finally, simulation results are presented to illustrate the effectiveness of our proposed controller.