This paper presents a robust image-based visual servoing design for a quad-rotor unmanned aerial vehicle performing a visual target-tracking operation in the presence of turbulent wind. Image information is extracted and processed to control the positioning and heading of the aerial vehicle. A novel adaptive non-singular fast terminal sliding mode strategy is introduced to manage the visual servoing error. Unlike other sliding mode methods, the proposed approach diminishes the complexity of the system due to the reduction of its control parameters while providing practical finite-time convergence, robustness against bounded external disturbances and model uncertainties, non-overestimation of the control gains, and chattering attenuation. Furthermore, the stability of the system in closed loop is guaranteed through Lyapunov theory. Finally, simulation results demonstrate the capabilities and performance of such a controller in a high-fidelity scenario using the Robot Operating System and Gazebo frameworks.

Metaheuristics are commonly used to solve complex and challenging problems, particularly in electrical system applications. Nevertheless, there is a colorful palette of metaheuristics to select from, which can be overwhelming for a practitioner with solid experience in controlling electrical systems. Still, it is well-known that empirical or analytical tuning of controllers is no trivial labor. This work implements a methodology to address the Metaheuristic Composition Optimization Problem to coin a heuristic-based technique that guides an initially random population of individuals to find the optimal set of parameters of an Adaptive Sliding Mode Controller in the search space. As a case study, we implement this methodology for controlling the dynamic response of a DC-DC Buck-Boost converter under different conditions, including nonlinear perturbations. The objective is clear: find the optimal heuristic-based solver to determine the control parameters satisfying a predefined performance. Our experimental results reveal the reliability and potential of the proposed methodology when finding suitable solutions for control system design applications. We also support our findings with statistical analyses performed on the results obtained by the tailored metaheuristic against other classical heuristic-based methods.

Contact-based aerial interaction control in unknown environments is a challenging issue. To solve this problem, this paper presents an image-based impedance control scheme for an aerial manipulator. Unlike points and image moments, the line features considered in this paper cannot uniquely determine the pose of the onboard camera. Therefore, a nonlinear observer is proposed to online provide supplementary 3D information of the system. After that, a hierarchical tracking controller, which is equipped with an image-space impedance filter, is designed to implement a trajectory tracking task with tunable compliance, while at the same time achieving a subtask that aims to determine the final pose of the aerial manipulator. By planning task-space trajectories, desired interaction behavior can be specified without relying on the global position of the system. Furthermore, the effectiveness criterion of the observer is derived, and the stability of the system is proven. Several experiments, including a push-and-slide task, robustness analysis, and a comparison with the state of the art have been conducted to demonstrate the feasibility of the method. Note to Practitioners—This article presents a method for controlling an aerial manipulator to interact with unknown environments without measuring the global position of both the environment and the aerial manipulator. Furthermore, unlike the existing methods, the proposed visual servoing method is based on line features, which are generally more robust than point features. Moreover, it would be intuitive for users to specify the approaching path and the interaction behavior of the aerial manipulator by just planning the image feature trajectories and the desired distance between the aerial manipulator and the environment. This distance is online estimated using the visual feedback of a monocular camera, instead of relying on any costly 3D-vision sensors or lidars, making it an economical solution for aerial manipulation in field environments.

This paper presents a finite-time visual servoing control strategy for the autonomous landing of a quadrotor onto a tilting and moving landing vehicle. The proposed method, called Finite-Time Dynamic Visual Servo (FTDVS) control, utilizes a computer vision technique called Virtual Reticle Image Plane (VRIP) to track four observable features on the landing plane called landmarks. To represent the sea state, target-plane tilting motion is modelled using monochromatic sinusoidal waves. VRIP exploits the sinusoidal tilting pose of the landing plane and calculates the time period of the tilting motion. Based on this time period, the proposed FTDVS control strategy enables the quadrotor to search for suitable landing windows on a tilting target plane. The proposed finite-time controller converges the tracking errors to zero in finite-time during a landing maneuver. First, a rigorous stability analysis of the FTDVS control law is presented, followed by simulations demonstrating acceptable performance in terms of tracking accuracy when compared to existing controllers for autonomous landing on moving targets. The proposed method has been experimentally validated in an indoor environment.