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.

This paper develops a novel homography-based visual servo control method for the vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) tracking the trajectory of a 6 degrees of freedom (6-DOF) moving ship. Different from the classical homography-based visual servoing which is only suitable for the trajectory tracking of a planar moving target, an extended homography-based visual servoing framework is proposed to track the trajectory of a 6-DOF moving target regardless of its time-varying rotational motion. Taking entries in the homography matrix as feedback, the visual dynamics of UAV is established and decoupled into a translational motion sub-dynamics and an attitude sub-dynamics based on the hierarchical control strategy. In the translational motion subsystem, the robust controller is designed by embedding the nonlinear differentiator, the adaptive method, and a smooth saturation model in the backstepping control scheme. The saturation model is introduced to generate the constrained control inputs to ensure the nonsingular attitude extraction and help the visual points remain in the camera’s field of view (FoV) simultaneously. In the attitude subsystem, a robust state-constrained control method is proposed by utilizing a robust control barrier function (RCBF), the quadratic programming (QP) and a saturation model to guarantee the visibility, where RCBF can accommodate unknown dynamics. The theoretical analysis demonstrates the asymptotic stability of the closed-loop system. Simulations are carried out to further validate the controller performance.

This paper addresses the spatial trajectory tracking problem for a stratospheric airship with state constraints, input saturation and unknown disturbances. First, a Laguerre-based model predictive kinematic controller (LMPC) is proposed to tackle the state constraints and generate the desired velocity signal. To reduce the complexity of online optimization, Laguerre functions are applied to decrease the number of optimization variables by approximating the predicted control sequence. Second, in the dynamic loop, a sliding mode controller (SMC) with fast power rate reaching law (FPRRL) is introduced to track the desired velocity signal. The unknown disturbances in the dynamic model of airship are estimated and compensated by reduced-order extended state observer (ESO). An anti-windup compensator is incorporated into the FPRRL-based SMC controller to deal with the input saturation. Stability analysis implies that the tracking errors converge to a small neighborhood of zero. Comparative simulations about spatial straight and curve trajectory tracking are provided to evaluate the effectiveness and robustness of the proposed control scheme.

This paper proposed an adaptive three-dimensional (3D) path-following control design for a robotic airship based on reinforcement learning. The airship 3D path-following control is decomposed into the altitude control and the planar path-following control, and the Markov decision process (MDP) models of the control problems are established, in which the scale of the state space is reduced by parameter simplification and coordinate transformation. To ensure the control adaptability without dependence on an accurate airship dynamic model, a Q-Learning algorithm is directly adopted for learning the action policy of actuator commands, and the controller is trained online based on actual motion. A cerebellar model articulation controller (CMAC) neural network is employed for experience generalization to accelerate the training process. Simulation results demonstrate that the proposed controllers can achieve comparable performance to the well-tuned proportion integral differential (PID) controllers and have a more intelligent decision-making ability.