This paper addresses the landing problem of a vertical take-off and landing vehicle, exemplified by a quadrotor, on a moving platform using image-based visual servo control. Observable features on a flat and textured target plane are exploited to derive a suitable control law. The target plane may be moving with bounded linear acceleration in any direction. For control purposes, the image of the centroid for a collection of landmarks is used as position measurement, whereas the translational optical flow is used as velocity measurement. The proposed control law guarantees convergence to the desired landing spot on the target plane, without estimating any parameter related to the unknown height, which is also guaranteed to remain strictly positive. Moreover, convergence is guaranteed even in the presence of bounded and possibly time-varying disturbances, resulting, for example, from the motion of the target plane, measurement errors, or wind-induced force disturbances. To improve performance, an estimator for unknown constant force disturbances is also included in the control law. Simulation and experimental results are provided to illustrate and assess the performance of the proposed controller.

This paper presents a nonlinear backstepping controller designed for an underactuated quadrotor slung load system that drives the load to a predefined trajectory. The quadrotor and the load are modeled as a rigid body and a point mass, respectively. The controller is designed through Lyapunov and backstepping techniques and the control laws for thrust and angular velocity are presented that guarantee the closed-loop system to be asymptotically stable. Experimental results are provided to assess the validity and performance of the proposed controller.

This brief addresses the design and experimental evaluation of a global controller to steer a quadrotor vehicle along a predefined path in the presence of constant wind disturbances. The proposed solution consists of a nonlinear adaptive state feedback controller for thrust and torque actuation that: 1) guarantees global convergence of the closed-loop path following error to zero in the presence of constant wind disturbances and 2) ensures that the actuation can be bounded as a function of the position and velocity errors without imposing a maximum for that bound, allowing for high performance control action. A prototyping and testing architecture, developed to streamline the implementation and tuning of the controller, is also described. Simulation results and experimental results, which include a hovering flight in the slipstream of a mechanical fan, are presented to assess the performance and robustness of the proposed controller.

This paper presents a solution to the problem of trajectory tracking control for autonomous surface craft (ASC) in the presence of ocean currents. The proposed solution is rooted in nonlinear model predictive control (NMPC) techniques and addresses explicitly state and input constraints. Whereas state saturation constraints are added to the underlying optimization cost functional as penalties, input saturation constraints are made intrinsic to the nonlinear model used in the optimization problem, thus reducing the computational burden of the resulting NMPC algorithm. Simulation results, obtained with a nonlinear dynamic model of a prototype ASC, show that the NMPC strategy adopted yields good performance in the presence of constant currents. Experimental results are also provided to validate the real-time implementation of the NMPC techniques for ASCs.