LAUSR

In this article, a constrained image-based visual servoing control method for the shipboard landing problem of unmanned helicopters is proposed. First, the pitch and roll motion of ship are predicted by an autoregressive (AR) model to determine an appropriate period for landing. Subsequently, a novel robust sliding mode controller without linear velocity measurements is developed on the basis of the perspective image feature in a virtual image plane. Meanwhile, a modified Chebyshev neural network (CNN) is proposed to estimate the uncertainties, including the linear acceleration of ship and translational perturbation, while an adaptive law is employed to compensate the influence of rotational disturbances. The whole controller only requires the measurements feedback of a vision sensor and an inertial measurement unit (IMU). Ulteriorly, to prevent the visual target on the ship from going beyond the field of view of camera, the constrained controller is developed by a control barrier function and a quadratic programming, where the unknown relative velocity is estimated by a velocity observer. Finally, simulations are implemented to substantiate the capability of the presented shipboard landing control method.