Control Of Unmanned Aerial Vehicles Research Articles

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: theory and experimental validation

This paper adopts a fixed-time method to study the trajectory tracking issue of a quadrotor unmanned aerial vehicle (UAV) under time-varying wind disturbances. Firstly, in order to address the impact of time-varying wind disturbances on UAV, a fixed-time disturbance observer is constructed to accurately estimate wind disturbances. Secondly, to improve control accuracy and convergence rate, a robust fixed-time controller is designed for the position and attitude system by combining the sliding mode control theory with fixed-time techniques. Furthermore, it is rigorously analyzed that the tracking error of the observer and controller can converge to zero via Lyapunov criterion, and the convergence time is independent of the initial state. Finally, the effectiveness and robustness of the designed control strategy are verified by numerical simulations and actual flight experiments, providing an effective connection between control theory and practical applications.

Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays

Consensus Tracking Control of Multiple Unmanned Aerial Vehicles Subject to Distinct Unknown Delays

Safe formation control of multiple unmanned aerial vehicles: control design and safety-stability analysis

Safe formation control of multiple unmanned aerial vehicles: control design and safety-stability analysis

Dynamics Event-Triggered-Based Time-Varying Bearing Formation Control for UAVs

This article addresses the leader-follower formation maneuver control problem of multiple unmanned aerial vehicles (UAVs), taking into account the time-varying velocity and time-varying relative bearing. An event-triggered bearing-based distributed velocity observer was designed, using only the desired position and velocity of the leaders. Furthermore, a dynamic event-triggered mechanism was introduced to reduce continuous communication between UAVs, thus effectively saving communication bandwidth and resources. Building on this, a bearing-only formation maneuver control strategy was proposed, integrating the event-triggered velocity observer with the backstepping control approach. To conclude, numerical simulations have been conducted to confirm the effectiveness of the proposed scheme in accomplishing formation maneuver control objectives, including translation, scaling, and rotation control. Furthermore, the advantages of the dynamic event-triggering strategy have been demonstrated through comparative simulations with traditional event-triggering strategies. Additionally, the effectiveness of the proposed observer and controller has been demonstrated by a comprehensive hardware-in-the-loop (HITL) simulation example.

Image fusion algorithm for the collaborative use of unmanned aerial vehicles

Currently, the applicable scope of unmanned aircraft application is increasingly expanding. The promising field of unmanned aircraft enhancement is the implementation of some collaborative actions during controlled flight. This paper considers some issues of the group application of unmanned aerial vehicles (UAV) related to the coordinated planning and control of UAVs performing surveillance missions. Performing aerial search operations is technically complicated by the requirement to recognize a search object in arbitrary conditions, which can be both simple and severe environment. The search area is limited by the UAV capabilities, so, in order to improve the efficiency of search operations, UAVs are combined into groups. An algorithm for solving the problem of object search in arbitrary conditions by a group of unmanned aircraft is proposed. The advantage of search by a group of unmanned aircraft is the coverage of the larger search area in a conventional unit of time. This paper addresses the unmanned aircraft configuration, containing both the means of collaborative flight operation and a synthetic vision system. The image obtained by the synthetic vision system is both a source of navigation information and a means which reliably determines the result of search operations. Depending on the conditions of search operations, the image obtained by the synthetic vision system may require additional processing to use as intended. A fusion algorithm is proposed, which is characterized by adaptive adjustment of parameters in each frame individually for different image fragments. Based on the results obtained, it is planned to create a new product for commercial operation of unmanned aircraft.

Planar curved path following controller for a small fixed-wing unmanned aerial vehicle with constrained parameters optimized by nonlinear model predictive control

Path following presents a pivotal challenge within the realm of small fixed-wing unmanned aerial vehicles. Firstly, a Lyapunov-stable path guidance law was formulated to follow specific planar curved paths. To ensure differentiability of the guidance law, a modified, smooth saturation function was derived. Secondly, an analysis was conducted to ascertain the interrelationship between control parameters and input constraints, thereby identifying the relevant parameter domains. Thirdly, the nonlinear model predictive control technique was harnessed to optimize both guidance law parameters, enhancing the unmanned aerial vehicle’s capacity to achieve optimal performance in both straight-line and circular path following, hereafter referred to as PFC_NMPC. By leveraging Lyapunov stability arguments for switched systems, the stability of the corresponding nonlinear switched system was guaranteed. In this study, square and circular paths were generated to assess the path-following control of a simulated fixed-wing unmanned aerial vehicle. The performance of various guidance laws, including those with fixed parameters (PFC), those with parameters tuned using fuzzy logic (PFC_FL), PFC_NMPC, vector field, and pure pursuit with line-of-sight, was compared. Notably, the proposed PFC_NMPC method exhibited the ability to expedite the unmanned aerial vehicle’s convergence to the desired path while maximizing the effective flight path length.

Heterogeneous Unmanned Swarm Formation Containment Control Based on Reinforcement Learning

In response to the problem of time-varying spherical formation control for a heterogeneous unmanned aerial vehicle (UAV) swarm system with dynamic uncertainty in the system model, this paper proposes an optimal distributed formation containment control method based on reinforcement learning. By combining the time-varying formation containment vector design with the distributed predefined time observer and constructing the augmented system of a multi-quadrotor UAV system and an observer, the problem of time-varying formation containment control in heterogeneous swarm systems is transformed into a stabilization problem. By introducing a value function with a discount factor, the stabilization problem of a heterogeneous UAV swarm system is transformed into an optimal control problem. Using the “actor-critic” neural network, combined with an off-policy reinforcement learning algorithm and distributed control methods, the solution to the formation containment controller is achieved in a data-driven manner. The stability of distributed observer and formation containment controller as well as the convergence of reinforcement learning algorithms are demonstrated using Lyapunov and related theory. The numerical simulation results demonstrate that the observation error of the designed distributed observer converges within a predefined time of 0.1 s, while the overall formation tracking error of the heterogeneous swarm converges within 2.74 s, thereby further validating the effectiveness and superiority of the designed control scheme.

Attack Detection and Security Control for UAVs Against Attacks on Desired Trajectory

The paper presents a security control scheme for unmanned aerial vehicles (UAVs) against desired trajectory attacks. The key components of the proposed scheme are the attack detector, attack estimator, and integral sliding mode security controller (ISMSC). We focus on malicious tampering of the desired trajectory sent by the ground control station (GCS) to the UAV by attackers. Firstly, we model attacks by analyzing the characteristics of desired trajectory attacks. Secondly, an integrated attack detection scheme based on an unknown input observer (UIO) and an interval observer is presented. Subsequently, a robust adaptive observer (RAO) is employed to compensate for the impact of attacks on the control system. Thirdly, an ISMSC with an attack compensation mechanism is established. Finally, simulation results are provided to verify the effectiveness of the proposed scheme. The proposed detection scheme can not only detect desired trajectory attacks but also distinguish them from abrupt unknown disturbances (AUDs). By utilizing ISMSC method, UAVs under desired trajectory attacks can fly safely. The proposed comprehensive framework of detection, estimation and compensation provides a theoretical basis for ensuring cyber security in UAVs.

Monitoring the State of the Operator of the Ergatic System in UAV Control Tasks

An important aspect of the application of unmanned systems is the reliability and safety of controlling these devices. An innovative approach has been proposed to improve the efficiency of the pilot operator and the reliability of the automated control system. It includes the development of an algorithm for determining the pilot’s condition based on heart rate analysis. This algorithm helps to assess the condition of the pilot and his ability to control the drone. Another important element of the proposed approach is the algorithm for selecting the control mode of the automated control system for unmanned aerial vehicles, which takes into account information about the functional state of the pilot operator. This algorithm allows the system to automatically switch between different operating modes depending on the condition of the pilot, ensuring optimal control and minimizing the risks of human error. An integrated approach to improving the reliability of the unmanned aerial vehicle control system allows not only improving the work of the pilot operator, but also ensuring the safer and more efficient operation of automated systems.

UAV control in autonomous object-goal navigation: a systematic literature review

Research interest in autonomous control of unmanned aerial vehicles (UAVs) has increased rapidly over the past decade. They are now widely used in civilian, military, and private areas. Applications include surveillance, search and rescue, and delivery tasks. More broadly, they excel at solving problems where a significant amount of space must be covered and traveled. However, using UAVs to solve navigation problems with full autonomy necessitates the mastering of complex subtasks. A solution that includes control, planning, localization, and mapping remains an open challenge. Object-goal navigation contains the same navigation problems where the main objective is to reach a target object. The search and identification of this target are central to the vehicle’s navigation. This requires an understanding of what it is and where it can be located to move around the scene. This paper presents a systematic literature review on object-goal navigation and its subtasks, using autonomous UAVs. Survey taxonomies were found for the tasks and methods behind navigation and target localization problems using UAVs. The review analyzed 67 articles found between 2011 and 2022. They were found in the ACM, IEEE Xplore, WebOfScience, Science Direct, and Scopus databases. This review revealed essential issues related to autonomous navigation task dependencies. Moreover, it highlighted gaps in UAV development and framework standardization. Open challenges for autonomous UAV control for object-goal navigation must address the research on finding methods for problems. For example, autonomy level and comparison metrics, considering safety, ethics, and legal implications.

A novel adaptive super-twisting trajectory tracking control with back propagation algorithm for a quadrotor UAV

This paper presents a new method for controlling a quadrotor unmanned aerial vehicle (UAV) with neural network adaptive adjustment combined with a super-twisting algorithm, which utilizes back-propagation algorithm in neural networks to design an adaptive method that can adjust the coefficients of the sliding mode surface as well as the control gain adjustment adaptive problem in the super-twisting to improve the stability and accuracy of the position and attitude control of the quadrotor UAV under uncertainty and external disturbances. Specifically, the adaptive neural network learns to dynamically adjust the sliding surface parameters and control gain, effectively inhibiting the sensitivity to parameter uncertainty and external disturbances, while the super-twisting sliding mode control ensures that the sliding trajectory converges in finite time and reduces the chattering phenomenon. In addition, the quadrotor UAV system is divided into a fully-actuated subsystem and an under-actuated subsystem, each of which contains two control inputs and the appropriate control algorithms are designed respectively, and the stability of the algorithm is demonstrated by means of a Lyapunov function in finite time. The proposed control method for quadrotor UAVs is validated through numerical simulations conducted in the Matlab/Simulink environment.

Radial basis function neural networks for formation control of unmanned aerial vehicles

Radial basis function neural networks for formation control of unmanned aerial vehicles

Distributed finite‐time backstepping adaptive containment control for multiple unmanned aerial vehicles with input saturation

AbstractThis paper investigates a distributed adaptive finite‐time containment control scheme for multiple unmanned aerial vehicles subject to external disturbances and input saturation. Combined with a novel indicator of the saturation degree designed by the hyperbolic tangent function, an adaptive method is proposed to deal with the input saturation issue, which considers both symmetry and asymmetry saturation. Moreover, to address the problem of the “explosion of terms” inherent in the traditional backstepping controller design, a fixed‐time sliding mode differentiator is utilized to approximate the derivative of the virtual signal in the command‐filtered backstepping method. Finally, the convergence of the errors and the practicability of the control law are verified by Lyapunov stability analysis and numerical simulations.

Order-supplementary finite-time trajectory tracking control of quadrotor unmanned aerial vehicles

Order-supplementary finite-time trajectory tracking control of quadrotor unmanned aerial vehicles

A systematic approach for threat and vulnerability analysis of unmanned aerial vehicles

The proliferation of Unmanned Aerial Vehicles (UAVs) is expected to experience a substantial rise in the next years, driven by their ever-increasing application across various domains. However, ensuring secure communication between UAVs and their ground stations is crucial to prevent the unauthorized disclosure of sensitive information that could jeopardize the mission’s integrity when exploited by malicious actors. Despite this importance, several UAV systems currently operate based on open-source command and control technologies, which have overlooked several security considerations while focusing on availability and safety. To address this concern, this study conducts a comprehensive security assessment of UAV-based systems starting with a systematic literature review whose main purpose is building a comprehensive catalog of threats associated with this technology. Particular attention has been paid to the MAVlink protocol, an open-source protocol commonly utilized for telemetry and command and control for multiple UAVs. Therefore, drawing upon the built catalog, a threat modeling and penetration testing technique has been employed to examine the MAVlink implementation on a real UAV. A threat model developed for a specific case study is also presented, leading to the discovery of four new vulnerabilities, some of which were successfully exploited through attacks. By shedding light on these vulnerabilities, this work seeks to encourage further investigation and research to develop robust security mechanisms for UAV communication systems. It is imperative to address these vulnerabilities proactively to enhance the overall security posture and safeguard against potential threats in the UAV ecosystem.

Event-triggered adaptive control for quadrotor unmanned aerial vehicle with prescribed performance

This paper proposes an event-triggered adaptive control algorithm with prescribed performance for quadrotor unmanned aerial vehicles (UAVs). The dynamic uncertainty and unknown external interference of the system are compensated by the property of approximation in a radial basis function (RBF) neural network. Based on this, introduce a performance constraint function to ensure that the trajectory tracking error of quadrotor UAV converges to a preset area, thereby guaranteeing transient and steady-state performance. In addition, design an event-triggered adaptive controller with the combination of the event-triggered mechanism, reducing the controller’s update frequency. Simulation results demonstrate that this algorithm exhibits strong anti-interference properties and effectively resolves the flight control problem of quadrotor UAV.

Vision-based finite-time prescribed performance control for uncooperative UAV target-tracking subject to field of view constraints

This paper presents a vision-based finite-time prescribed performance controller for unmanned aerial vehicle (UAV) tracking of uncooperative aerial targets. The relative states between UAV and target are estimated by an onboard monocular camera. The inability of visual measurements to accurately determine the initial state of the target renders conventional prescribed performance controllers ineffective in such situations. As a result, it becomes essential to address the problem of prescribed performance control under conditions of uncertain initial values By utilizing an auxiliary transforming function, an Asymmetric Barrier Lyapunov Function (ABLF) and a finite-time prescribed performance function, a robust adaptive controller based on backstepping framework is proposed to deal with state constraints under unknown initial tracking conditions. It is proved that, the closed-loop relative position is capable of reaching the prescribed performance bound before the preset transforming time and converging to the prescribed steady-state error before a finite setting time. Simulation examples are provided to illustrated the effectiveness of the proposed tracking algorithm.

Robust fixed-time flight controller for a dual-system convertible UAV in the cruise mode

This paper investigates the attitude tracking control problem for the cruise mode of a dual-system convertible unmanned aerial vehicle (UAV) in the presence of parameter uncertainties, unmodeled uncertainties and wind disturbances. First, a fixed-time disturbance observer (FXDO) based on the bi-limit homogeneity theory is designed to estimate the lumped disturbance of the convertible UAV model. Then, a fixed-time integral sliding mode control (FXISMC) is combined with the FXDO to achieve strong robustness and chattering reduction. Bi-limit homogeneity theory and Lyapunov theory are applied to provide detailed proof of the fixed-time stability. Finally, numerical simulation experimental results verify the robustness of the proposed algorithm to model parameter uncertainties and wind disturbances. In addition, the proposed algorithm is deployed in a open-source UAV autopilot and its effectiveness is further demonstrated by hardware-in-the-loop experimental results.

Brain–Computer Interface Based on Motor Imagery With Visual Guidance and its Application in Control of Simulated Unmanned Aerial Vehicle

Brain–Computer Interface Based on Motor Imagery With Visual Guidance and its Application in Control of Simulated Unmanned Aerial Vehicle

VIDEO REPEATER DESIGN CONCEPT FOR UAV CONTROL

This study is aimed at the utilization of a video repeater for unmanned aerial vehicle (UAV) control, involving a comparative analysis of existing scientific literature, methodologies, and available solutions. Through comparative analysis, the method of using an external repeater emerged as the most promising, offering flexibility in selecting repeaters with superior performance and advanced capabilities. Notably, this approach allows for the utilization of a single repeater across multiple UAVs and facilitates convenient modification or upgrade of the repeater without necessitating alterations to the UAV itself. Following component selection, an experimental prototype was designed to facilitate empirical investigations. Additionally, a frequency transmission scheme was devised for quadcopter control employing the repeater. This research contributes to advancing UAV control systems by introducing a novel approach to video repeater integration, poised to enhance operational efficiency and adaptability in diverse settings. The findings from this study are anticipated to inform further developments in UAV technology, particularly in optimizing video transmission for improved situational awareness and mission effectiveness.