Accurate force estimation is essential for robust control and improved model understanding in tethered aerial vehicles operating under uncertain wind conditions. These systems exhibit highly nonlinear and strongly coupled dynamics influenced by aerodynamic disturbances and tether forces. Extended Kalman Filters (EKF) are commonly used for joint state and disturbance estimation; however, their performance strongly depends on fixed process (Q) and measurement (R) noise covariance tuning.This paper proposes an adaptive covariance tuning strategy for unknown force estimation in tethered aerial systems modeled in spherical coordinates. Innovation- and residual-based statistics are used to update the Q and R matrices online. The approach is evaluated through simulation under multiple operating scenarios and validated using experimental flight data. Results show that selective covariance adaptation, particularly process noise adaptation, improves disturbance estimation accuracy while preserving filter stability.

This research investigates the use of a Magnus-effect winged quadcopter for tethered UAV applications in airborne wind energy (AWE) systems, which harness wind energy at higher altitudes. Tethered flight in AWE systems is highly nonlinear and uncertain, requiring robust control strategies, especially during take-off and landing in turbulent winds. This work explores various control approaches, including Feedback Linearization with PID or sliding mode control, with and without feedforward disturbance compensation. The proposed controllers are tested in realistic simulations, indoor experiments at Gipsa-lab, and outdoor tests, demonstrating robust performance in varying wind conditions. (c) 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Tethered drones have proven effective across various applications, one of which is the core focus of this work: harnessing clean wind energy through airborne wind energy (AWE) systems. A significant body of research work in the literature focuses on optimizing the power generation phase. However, automating take-off and landing remains a pressing challenge within the AWE community, emphasizing the importance of automating these processes for commercial viability. Reliable automation of these phases is crucial for ensuring system safety and operational efficiency, particularly given the variability in wind conditions that can necessitate frequent landings and take-offs. This work focuses exclusively on the critical phases of take-off and landing for a Magnus-effect winged quadcopter AWE system. A thrust optimization-based control allocation considering the Magnus-effect wings as actuators is proposed in this article. The performance of this control strategy in enabling the system to take off and land under turbulent conditions and a wide range of wind speeds has been validated experimentally under real conditions.

This article introduces a novel self-triggering strategy designed to ensure the control of discrete-time linear systems with guaranteed stability, even in the presence of disturbances and uncertainties. This strategy aims to consistently maintain satisfaction of state constraints while accounting for the uncertainties in the system through a set-membership description. The self-triggering framework primarily relies on reachable and invariant sets. Reachable sets quantify the maximum deviation of the disturbed system from the predicted behavior, while an invariant set establishes triggering bounds for these reachable sets. This control method is intended to minimize the number of measurements required, thereby avoiding network bandwidth saturation. To validate the effectiveness of the proposed strategy, the experiments are conducted on an air extractor system, demonstrating a reduction in the number of measurement samples while ensuring stability and satisfying system state constraints.

Art is one of the oldest forms of human expression, constantly evolving, taking new forms and using new techniques. With their increased accuracy and versatility, robots can be considered as a new class of tools to perform works of art. The STRAD (STReet Art Drone) project aims to perform a 10-meter-high painting on a vertical surface with sub-centimetric precision. To achieve this goal we introduce a new design for an aerial manipulator with elastic suspension capable of moving from one equilibrium position to another using only its thrusters and an elliptic pulley-counterweight system. A feedback linearization control law is implemented to perform fast and accurate winding and unwinding of an elastic cable.

This study presents an new robotic airborne solution for autonomous wall painting using a pointillism technique. The proposed dot-painting drone is a quadcopter equipped with an additional forward propulsion unit and a spring-mounted painting pad. It is designed to bounce on a vertical wall in order to print dots at a controlled frequency along a predefined trajectory. A dynamic model of the system is derived and used to control accurately the bouncing frequency as well as the position of the robot. The performance of the system is validated experimentally, demonstrating successful indoor painting capability of pointillism drawing on vertical walls. This work represents a first step toward fully autonomous, large-scale mural reproduction using aerial robotics.

While multirotor autonomous aerial vehicles have excellent maneuverability, they lack the ability to perform long-endurance flights. Many design-based approaches to addressing this drawback exist. To overcome this challenge, this article proposes the Magnus-effect winged quadcopter system design. We use the rotational speed of the Magnus-effect-based wings in this system as a control variable to maximize the contribution from these wings, thus minimizing the necessary and required thrust from the quadcopter and, therefore, the system's energy consumption. To this end, we developed an airspeed-dependent nonlinear optimization control allocation scheme to operate the system at a wide range of airspeeds. Realistic simulations and outdoor experiments validate the approach, demonstrating the superior energy efficiency of the Magnus-based quadcopter system compared to traditional quadcopter and emphasizing its potential for achieving extended endurance.

In this paper we present experimental results concerning the impact of the chosen torque control gain on the deterioration of the drive-train in wind turbines. Here the deterioration is considered to be produced by the mechanical stress, on the drive-train, and it is measured as the dissipated power at this level. The wind turbine is assumed to be controlled, into the variable-speed region, by using the optimal torque control method, commonly used for maximum power tracking. In this work, we present and discuss the obtained results of produced energy and dissipated one (at the drive-train level), for different choices of the torque control gain under the presence of high turbulence intensity of the wind speed. The tests have been performed in simulation by using real wind-speed data and by a combined simulated drive-train and a real mini wind turbine available at GIPSA-lab. Copyright (c) 2024 The Authors.

Estimating distance traveled is a frequently arising problem in robotic applications designed for use in environments where GPS is only intermittently or not at all available. In UAVs, the presence of weight and computational power constraints makes it necessary to develop odometric strategies based on minimilastic equipment. In this study, a hexarotor was made to perform up-and-down oscillatory movements while flying forward in order to test a self-scaled optic flow based odometer. The resulting self-oscillatory trajectory generated series of contractions and expansions in the optic flow vector field, from which the flight height of the hexarotor could be estimated using an Extended Kalman Filter. For the odometry, the downward translational optic flow was scaled by this current visually estimated flight height before being mathematically integrated to obtain the distance traveled. Here we present three strategies based on sensor fusion requiring no, precise or rough prior knowledge of the optic flow variations generated by the sinusoidal trajectory. The “rough prior knowledge” strategy is based on the shape and timing of the variations in the optic flow. Tests were performed first in a flight arena, where the hexarotor followed a circular trajectory while oscillating up and down over a distance of about [Formula: see text] m under illuminances of [Formula: see text] lux and [Formula: see text] lux. Preliminary field tests were then performed, in which the hexarotor followed a longitudinal bouncing [Formula: see text]-long trajectory over an irregular pattern of grass.

This paper evaluates experimentally a novel strategy for solving a variant of the differential game of target defense in presence of obstacles. The game is widely applied in the areas of military defense for protecting important equipment such as a ship, an aircraft, a moving vehicle, or a sensitive installation from a malicious attacker. The state‐of‐the‐art approaches mostly employ an offline optimization strategy that is only applicable to holonomic robots. Moreover, most of the approaches could not autonomously avoid obstacles or take into account uncertainties. As a consequence, this paper presents an online optimization technique, by designing a trade‐off parameter that integrates game theory with the model predictive control, which allows a nonholonomic defender to intercept the attacker while simultaneously defending the target. Simulations under different conditions as well as several indoor laboratory experiments validate the proposed approach. Moreover, performance is compared with a standard model predictive control approach.

In this letter, we describe a new light-flashing shield to be used at flash-based imaging or event-based vision tasks performed in environments with poor light conditions. The shield incorporates a multiplexer that permits setting of the operation mode, the nominal current, the pulse width, and the trigger sensitivity of up to 2 high-brightness LEDs by means of a single inter-integrated circuit (I2C) bus. A trigger conditioning circuit permits synchronization of the LEDs light and an Intel RealSense d455 camera to obtain a set of clear images while the LEDs are illuminated, this feature being the main novelty and contribution of this work, in order to provide light on-demand in visual-based tasks performed in environments with poor light conditions. Images obtained during real tests prove the correct synchronization between the light-flashing shield and the camera.

Tethered flight is a highly nonlinear and uncertain process that requires robust control approaches to master its operation. However, there have been only a few researches on the control of the take-off and landing phases of these systems. This paper proposes a sliding mode controller, for tethered drones, to track a desired flight trajectory. Additionally, a three-dimensional Extended Kalman filter is integrated into the control strategy to estimate and compensate for aerodynamic disturbances. Controller performance is evaluated against wind turbulence conditions and modeling uncertainties. The results are compared with those of a non-linear feedback linearization controller.

This paper evaluates experimentally a novel strategy for solving a variant of the differential game of target defense in the presence of obstacles. The state-of-the-art approaches mostly employ an offline optimization strategy that is only applicable to holonomic systems. This paper presents an online optimization technique, by designing a trade-off parameter that integrates game theory with the model predictive control which allows a nonholonomic defender to intercept an attacker while simultaneously defending a specific target. Several indoor laboratory experiments validate the performance of the proposed approach and compared with a standard model predictive control approach.

This paper presents the experimental validation of a real-time nonlinear model predictive control algorithm developed to deal with dynamic and static obstacle avoidance for a non-holonomic wheeled mobile robot. Unlike state-of-the-art techniques, the speed of the dynamic obstacle is unknown to the controller. The developed controller autonomously drives the robot away from the obstacle by calculating the minimal distance from which the avoidance maneuver starts. Several real-time experimental results for stabilizing a mobile robot in presence of dynamic and static obstacles are presented.

This paper proposes a nonlinear control strategy to achieve autonomous take-off and landing of a drone-based tethered Magnus flying device. This flying device is used in airborne wind energy system that converts wind energy into electricity. A 3D model is constructed and used to design a feedback linearization controller to obtain the desired flight trajectories. Simulation results with an illustrated realistic model indicate good performance and robustness in different flying conditions. This complex and realistic simulation environment supports real experimental testing.

Airborne Wind Energy systems (AWE) represent a promising solution to environmental challenges that has revolutionized research in the wind industry. The studied AWE system in this work is equipped with a multicopter drone in order to perform take-off and landing maneuvers and the objective consists in presenting an estimation strategy based on an Extended Kalman Filter (EKF) to obtain accurate estimation of the aerodynamic forces needed to improve the performance of the proposed control law for the considered prototype in this study. The proposed method is implemented and tested in a numerical and experimental environment. The obtained results show the effectiveness of the introduced method at estimating unknown forces that act on the system despite the presence of several sources of uncertainty: neglected nonlinearities, poorly known parameters, physical constraints, etc. Moreover, we show that the knowledge of these forces allows one to improve the robustness of the studied AWE system during its take-off and landing phases.