Nowadays, with the increasing popularity of quadcopter unmanned aerial vehicles in several real-world applications, achieving a fully autonomous quadcopter flight has become an imperative topic investigated in many studies. One of the most pressing issues in such a topic is the precision landing task, which always is devastatingly influenced by the ground effect and external disturbances. In this paper, we present an autonomous quadcopter landing algorithm allowing the vehicle to land robustly and precisely onto a heaving platform. Firstly, a robust control algorithm addressing the altitude flight under the ground effect and external disturbances is derived. We strictly prove the closed-loop system stability by using the Lyapunov theory. Secondly, a landing target state estimator is proposed to provide state estimations of the moving landing target. In addition, we propose a landing procedure to ensure the landing task is achieved safely and reliably. Finally, we use a DJI-F450 drone equipped with an infrared sensor and a laser ranging sensor as the experimental quadcopter platform and conduct experiments to evaluate the performance of our new algorithm in real flight conditions. The experimental results demonstrate the effectiveness of the proposed method.

The aim of this paper is to propose a new more efficient self-organizing fuzzy cerebellar model articulation controller (CMAC) for uncertain nonlinear systems. The proposed controller uses a prior Gaussian membership function (GMF) and a present GMF on each layer of CMAC to make a new mixed GMF. Then the inputs can concurrently stir the present state and prior state to regulate the suitable errors so that the new self-organizing fuzzy CMAC (NSOFC) is able to learn the parameters more efficient, reduce the computational loading, anticipate the subsequent state of the inputs, and automatically develop the structure of the NSOFC. The sliding mode control is used to simplify the inputs. By using the self-organizing structure in the NSOFC, the layers will be increased or decreased systematically. The control system consists of an NSOFC and a compensation controller. The NSOFC is the main tracking controller, which imitates an ideal controller, and the compensator expels the approximation error. An adaptive PI method is utilized to online update the parameters for the proposed NSOFC in order to achieve more efficient control performance. The studies of an inverted double pendulum system point out that favorable tracking performance is obtained by using the proposed method.