Adaptive neural network control for a quadrotor landing on a moving vehicle

Abstract

An autonomous vehicle landing control algorithm of a quadrotor is investigated for the situation when the quadrotor hovers above the vehicle in this paper. To facilitate the controller design, the problem of autonomous landing is converted from general trajectory tracking problem of a quadrotor to a stabilization problem of relative motion. A four-degrees-of-freedom (4-DOF) nonlinear relative motion model with four control inputs is estimated. An adaptive radial basis function neural network (RBFNN) is developed to estimate the unknown disturbance and is applied to design the controller through a backstepping technique. It is proved that all the states in the closed-loop system are uniformly ultimately bounded and the error converges to a small neighborhood of origin. Numerical simulation results illustrate the good performance of the proposed controller.