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Unmanned Aerial Vehicles (UAVs) encounter wind gusts during outdoor operations, impacting their position holding, particularly for quadrotors. This vulnerability is amplified during the autonomous docking to outdoor charging stations. The integration of real-time wind preview information for UAV gust rejection control has become more feasible with advances in remote wind sensor technologies like LiDAR. In this study, a ground-based LiDAR system is proposed to predict wind gusts at the landing site of quadrotors. The acquired wind preview data are subsequently utilized by the Model Predictive Control (MPC) to effectively mitigate disturbances. To validate the proposed methodology, a nonlinear simulation environment has been established using LiDAR data collected from comprehensive field tests. The results demonstrate a notable improvement in the system performance compared to benchmark results. This research underscores the practical utility of real-time wind preview information, facilitated by LiDAR technology, in enhancing the overall operational resilience of UAVs, especially quadrotors, during challenging environmental conditions.

Autonomous navigation in global positioning system (GPS)-denied environments is one of the challenging problems in robotics. For small flying robots, autonomous navigation is even more challenging. These robots have limitations such as fast dynamics and limited sensor payload. To develop an autonomous robot, many challenges including two-dimensional (2D) and three-dimensional (3D) perception, path planning, exploration, and obstacle avoidance should be addressed in real-time and with limited resources. In this paper, a complete solution for autonomous navigation of a quadrotor rotorcraft is presented. The proposed solution includes 2D and 3D mapping with several autonomous behaviors such as target localization and displaying maps on multiple remote tablets. Multiple tests were performed in simulated and indoor/outdoor environments to show the effectiveness of the proposed solution.

This paper presents a dynamic image-based visual servoing (IBVS) control law for a quadrotor unmanned aerial vehicle (UAV) equipped with a single fixed on-board camera. The motion control problem is to regulate the relative position and yaw of the vehicle to a moving planar target located within the camera’s field of view. The control law is termed dynamic as it’s based on the dynamics of the vehicle. To simplify the kinematics and dynamics, the control law relies on the notion of a virtual camera and image moments as visual features. The convergence of the closed-loop is proven to be globally asymptotically stable for a horizontal target. In the case of nonhorizontal targets, we modify the control using a homography decomposition. Experimental and simulation results demonstrate the control law’s performance.

This paper proposes a trajectory planning and control strategy to optimally visit a given set of waypoints in the presence of wind. First, aerodynamic properties of quadrotors which affect trajectory planning and tracking performance are investigated. Blade flapping, induced and parasitic drag are derived and an extended method to identify all coefficients from flight test data is developed. Then, a three-step approach is suggested to optimize the trajectory. These steps reduce the size of the optimization problem and thereby increase computational efficiency while still guaranteeing near optimal results. The trajectories are optimized for minimal aerodynamic drag and minimal jerk. The derived smooth trajectory generation is compared with traditional trajectory planning consisting of discrete point to point tracking followed by low-pass filtering. The new trajectories yield a clear reduction in maximal needed thrust and in Euler angle aggressiveness. A thrust vectoring controller is designed, which exploits the a priori trajectory information and identified aerodynamic properties. Its performance is compared to a standard PID controller and results show a reduction in tracking delay and an increase in thrust and attitude angle margins, which overall enable faster flight.

The formation control problem in multi-quadrotor systems is studied in this paper. Each quadrotor has limited access to its neighbors’ information due to communication constraint. First, the dynamics model of the quadrotor is linearized using Newton–Euler method. The distributed formation control law is then designed and the stability analysis is provided. An experimental platform is built with three Parrot Bebop drones and an indoor motion capture system. Numerical and experimental results are provided to show the effectiveness of the proposed algorithms.

Vision-based tracking of a moving target is achieved in three steps: image processing, coordinate recovery and trajectory following. A testing platform containing two spawned quadrotors as the target and the tracker is developed on ROS, from which sequences of image frames are generated by an on-board camera while the target moves along a given path on the ground. A simple image processing algorithm is developed that achieves visual tracking and is robust to image blur and other background noises. Coordinate recovery recovers the original target trajectory as the desired path of the tracker in the real-world. A target following control law moves the tracker along the desired trajectory point-by-point. An analysis of the robustness of the tracking approach with respect to image blur and measurement noise of the tracker position is carried out to assure the applicability of the approach to real-world scenarios.

Flexible and robust Time-Varying Formation (TVF) tracking of Unmanned Ground Vehicles (UGVs) guided by an Unmanned Aerial Vehicle (UAV) is considered in this paper. The UAV–UGVs system control model is based on leader-follower approach, where the control scheme consists of two consecutive tasks, namely, deployment task and TVF tracking. Accordingly, two novel nonlinear controllers are proposed for controlling the UGVs formation. First, unlike the classical frameworks on UGVs formation tracking, for which only particular shapes are handled (e.g. circle, square, ellipse), we propose a UGVs deployment-controller ensuring to reach free-formation shapes. The key feature is in using the estimated implicit representation of the desired formation shape as a potential function to generate the UGVs reference trajectory. Second, in the TVF tracking task, a robust cascaded velocity/torque controller for UGVs is proposed based on kinematic and dynamic models. Differently from the classical backstepping framework, the key idea is in introducing an auxiliary control input, in such a way that the overall UGV dynamics is converted into a simpler and modular control structure. As such, the auxiliary input is used to control indirectly the actual UGVs velocity vector. A signum term is added to the torque-input to compensate for the unknown external disturbances and unmodeled dynamics. Numerical simulation shows the effectiveness of the proposed formation controllers compared with the case when the perfect velocity-tracking assumption holds. Experimental results are further provided using three festos Robtino robots to show the validity of the proposed TVF tracking velocity-control scheme.

Micro-Unmanned Air Vehicles (Micro-UAVs), especially quadrotor UAVs, have been widely used in recent years because of their simple structure and convenient operation. However, due to under-actuated and strongly-coupled characteristics, their flight performance is not perfect when suffering external uncertainty such as wind disturbance. Therefore, in this paper, the literature is reviewed related to wind acting on UAVs and makes some conclusions. First, we review the wind field model of quadrotor operation and decompose it into wind turbulent model, wind shear model, and other typical wind field models. Then we focus on the mathematic model of wind disturbance on UAVs and divide it into effect on rotors and effect on the fuselage. Finally, various control laws are used to study the wind resistance of quadrotors. We review the promising works which have been done before and determine the research emphasis to be focused on in the future.

Unmanned Aerial Vehicles (UAVs) have the potential to perform many different missions, some of which may require a large aircraft for endurance and a small aircraft for maneuverability in wind gusts or cluttered environments such as buildings. This paper proposes a novel combination of a quadrotor and a hybrid biplane capable of joint hover, joint forward flight, and mid-air separation followed by separate flight. We investigate cooperative control strategies during joint flight that do not require any communication between the quadcopter and the biplane. This means that the two aircraft have their own independent control strategy based on their own sensors. The biplane, which is the largest of the two with most control authority, leads the flight and the goal for the quadrotor is to help in producing thrust and increasing rotational stability. Three control strategies for the quadrotor are compared: a proportional angular rate damper, a proportional angular acceleration damper, and constant thrust without attitude control. Simulation and practical tests show that for desired attitude changes of the biplane, the quadrotor rate- and angular acceleration damper strategies lead to a small performance degradation. However, the angular rate damper strategy reduces the roll angle error in disturbance rejection experiments and requires the smallest input command. The in-flight release is successfully tested in joint hover up to a forward pitch angle of −18^∘.

In this paper, a novel robust backstepping-based fault-tolerant control is designed, implemented and validated experimentally on a quadrotor unmanned aerial vehicle testbed under actuator fault conditions for tracking control. Backstepping is known as a robust method to maintain system performance and keep it insensitive to disturbances. The proposed nonlinear controller is mainly based on the advanced robust backstepping method to solve the system uncertainties caused by disturbances and actuator faults, which appear in the motor part of the system and are compensated without diagnostics or fault identification. The various parameters of the proposed approach are optimized using the particle swarm optimization (PSO) method. Owing to the minimized control effort to accommodate uncertainties compared to the conventional backstepping, the proposed approach can still maintain the system performance when severer faults occur. The proposed approach is validated, first, in a simulation using the nonlinear model of the quadrotor, then experimentally, using the Quanser 3DOF Hover quadrotor. Both theoretical and experimental analyses have demonstrated that the effectiveness of the proposed improved backstepping fault-tolerant control strategies faces significant loss of actuator efficiency.

This paper studies the decentralized leader-following control of a heterogeneous swarm robotic system composed of a group of quadrotors as aerial agents, and a group of two-wheeled mobile robots as ground agents. The interaction mechanism between the agents is based on the method of artificial potential fields. While the agents in the aerial and ground sub-swarms communicate with each other through local and direct vision, the information exchange between the aerial and ground sub-swarms is established through a wireless network. The control system of ground and aerial agents is designed using the sliding mode control technique. The controller proposed for quadrotors is finite-time, integral-terminal, and valid for both small and large angles of attitude. The effect of the communication imperfections of the wireless network on the leader-following behavior of the heterogeneous robotic swarm is investigated. Subsequently, an innovative component, called the trajectory predictor, is designed to compensate for the undesirable impacts caused by the communication imperfections of the wireless network. Finally, the performance of the presented collective control strategy is evaluated via several numerical simulations.

The wide spectrum of recent applications for UAVs imposes further challenges to their abilities and control. This is especially true when operating in harsh and hostile environments where disturbances are huge and actuators are prone to failure. Conventional systems and traditional control techniques are not sufficient for stability and tracking under these circumstances. This paper proposes a unique innovative overactuated quadrotor system that has six DoFs. The vehicle has four rotors and each rotor can tilt independently in the $YZ$-plane. It has the ability to correct its position and attitude in a decoupled way which is different from the conventional quadrotor configurations. Sliding-mode control associated with switching control mode is used for the control algorithm of the system. The system shows agility in the face of large disturbances and robustness against actuator failure which makes it a perfect fit for extreme and challenging environments. The concept of the system and its ability are illustrated in simulation with promising results.

This study investigates the fault detection and fault estimation problem of a quadrotor with disturbances. A synthesized design of adaptive and sliding-mode observer is proposed to address the effective detection and estimation of incipient faults. First, the decomposed subsystems are obtained through the coordinate transformation, and the initial and incipient faults are separated from the disturbances. Second, an adaptive observer is applied to the decomposed unperturbed subsystem to estimate incipient faults, while the sliding-mode observer remains robust to disturbances for the perturbed subsystem. Lyapunov stability theory ensures the convergence of dynamic errors and the stability of the quadrotor system. Finally, the effectiveness of the proposed synthesized algorithm of incipient fault detection is verified by the quadrotor simulation.

In this paper, a fully actuated system approach (FASA)-based control scheme is proposed for the trajectory tracking of a quadrotor unmanned aerial vehicle (UAV). System uncertainty, external disturbance and actuator constraint are all considered, which make the problem challenging. Inspired by the active disturbance rejection control (ADRC), tracking differentiator (TD) and extended state observer (ESO) are introduced for handling the uncertainties and generating the feedback signals. With the proposed feedback control law, the performance of the resulted closed loop system is related to its eigenstructure - eigenvalue and eigenvectors. Based on a type of control parametrization method, the parametrized eigenstructure of the closed loop system are optimized. A better performance is observed by comparative numerical simulation.

This paper proposes an optimal output feedback tracking control scheme of the quadrotor unmanned aerial vehicle (UAV) attitude system with unmeasured angular velocities and model uncertainties. First, neural network (NN) is used to approximate the model uncertainties. Then, an NN velocity observer is established to estimate the unmeasured angular velocities. Further, a quadrotor output feedback attitude optimal tracking controller is designed, which consists of an adaptive controller designed by backstepping method and an optimal compensation term designed by adaptive dynamic programming. All signals in the closed-loop system are proved to be bounded. Finally, numerical simulation example shows that the quadrotor attitude tracking scheme is effective and feasible.