Stability Of Unmanned Aerial Vehicle Research Articles

Analysis and control of an active balance tail for the tilting dual-rotor UAV

PurposeCompared to quad-rotor unmanned aerial vehicle (UAV), the tilting dual-rotor UAV is more prone to instability during exercises and disturbances. The purpose of this paper is using an active balance tail to enhance the hovering stability and motion smoothness of tilting dual-rotor UAV.Design/methodology/approachA balance tail is proposed and integrated into the tilting dual-rotor UAV to enhance hovering stability and motion smoothness. By strategically moving, the balance tail generates additional force and moment, which can promote the rapid stability of the UAV. Subsequently, the control strategy of the UAV is designed, and the influence of the swing of the balance tail at different installation positions with different masses on the dual-rotor UAV is analyzed through simulation. The accompany motion law and the active control, which is based on cascade Proportion Integration Differentiation (PID) control to enhance the hovering stability and motion smoothness of the UAV, are proposed.FindingsThe results demonstrate that active control has obvious adjustment effectiveness when the UAV moves to the target position or makes an emergency stop compared with the results of balance tail no swing and accompany motion.Practical implicationsThe balance tail offers a straightforward means to enhance the motion smoothness of tilting dual-rotor UAV, rendering it safer and more reliable for practical applications.Originality/valueThe novelty of this works comes from the application of an active balance tail to improve the stability and motion smoothness of dual-rotor UAV.

Numerical modelling of aerodynamic response to gusts and gust effect mitigation

Unmanned aerial vehicle (UAV) flights in urban environments are challenging due to the complex flow structures and elevated turbulence around buildings. Consequently, research has shifted towards investigating the impact of gusts on the aerodynamic stability and control of UAVs. This study focuses on enhancing gust numerical modelling capabilities to understand the aerodynamic response, specifically exploring gust mitigation strategies for UAVs operating in turbulent urban environments. The split-velocity method, originally designed for two-dimensional compressible inviscid flows, where the velocity components were decomposed into a prescribed gust velocity and the remaining velocity components, is extended to three-dimensional incompressible viscous flows. To facilitate effective gust mitigation techniques, a radial basis function is applied to the modified split-velocity method to numerically model wings in pitching motions under gust encounters. A novel strategy is proposed to correct the discretized gust velocities and ensure gust flux conservation, showing effective improvement to the numerical predictions. The computed results agreed well with the experimental data available in the public domain, confirming that wing pitch motion can effectively mitigate the effects of gusts.

Modelling and simulation of quadcopter UAV based on PID control

In the evolving field of aerial robotics, the precise control and stabilization of Unmanned Aerial Vehicles (UAVs), particularly quadrotors, stand as critical challenges due to their inherent nonlinear dynamics and susceptibility to external disturbances. This study embarks on addressing these challenges by first establishing a comprehensive mathematical model of a quadrotor's dynamics utilizing the Newton-Euler formulation. This foundational model serves as the basis for implementing a Proportional-Integral-Derivative (PID) control strategy, aimed at maintaining the UAV's desired flight trajectory and enhancing its stability against environmental perturbations. The effectiveness of the PID control approach is meticulously evaluated through a series of simulation tests conducted within the MATLAB/Simulink environment, focusing on the quadrotor's pitch, roll, and yaw control channels. The outcomes of these simulations provide convincing evidence of the PID scheme's ability to ensure rapid response times and precise attitude regulation, underpinning its viability for robust quadrotor UAV operations across diverse operational scenarios. Moreover, this research not only establishes a critical step towards the autonomous operation of UAVs but also accentuates the significance of sophisticated control mechanisms in the advancement of aerial robotics technology.

Effect of leading-edge protuberances on swept wing aircraft performance

Stall is a complex phenomenon in aircraft that must be suppressed during flight. As a novel passive control method, bionic leading-edge protuberances (LEPs) have attracted widespread interest, particularly for delaying stall. Bionic protuberances at the leading edge of airfoils have been designed to control stall and increase the stability of unmanned aerial vehicles during operation, and it is the flow control mechanism associated with this application that is investigated in this study. First, numerical simulations are conducted to obtain the aerodynamic characteristics of original and bionic airfoils based on the S1223 large-lift airfoil. Next, the impact of the LEP amplitude is investigated. Finally, a vortex definition parameter, the Liutex vector, is utilized to analyze the influence of LEPs on vortices. The results show that bionic LEPs inspired by those on humpback whale flippers can improve the aerodynamic performance of airfoils under the extreme conditions that exist after stall, resulting in an ∼22% increase in the lift–drag ratio. LEPs are found to segment the flow field near the wing surface. The flow becomes bounded between adjacent protuberance structures, significantly inhibiting the development of flow separation and providing a drag reduction effect. This study thus provides a new approach for improving aircraft performance.

The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs.

This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances.

On the use of PID control to improve the stability of the quad-rotor UAV

The recent development of drones has aroused great concern at home and abroad. For civil industry and agricultural plant protection industry, it can provide safer and more stable flight and improve production efficiency. For the exploration industry, the application of UAV (Unmanned Aerial Vehicle) will reduce unnecessary casualties. In places where damage is serious, UAV will bring accurate on-site information and data more quickly. It can make drones to replace people in high-risk operations, scientific and technological agricultural development and survey more stable and safe; For the military industry, it can be applied to different combat environments to increase its stability. In recent years, although UAV combat and reconnaissance are not the mainstream combat methods, UAV has become or an indispensable part. Improving the stability of UAV will also increase the military combat capability, so UAV has good development prospects and economic benefits. Nowadays, PID (Proportion Integration Differentiation) control plays an indispensable role in quad-rotor UAV. How to use different types of PID control combined with existing intelligent technology to improve the stability of quad-rotor UAV is a hot issue in recent years. This paper summarizes the previous research results in this field, briefly describes the motion mechanism of four-rotor UAV, wind disturbance resistance mechanism, the action principle and contract between traditional PID control and fuzzy PID control, as well as the effect of intelligent algorithm on PID control addition, and puts forward suggestions for PID control algorithm of the future UAV.

Design and stabilization of a Coandă effect-based UAV: Comparative study between fuzzy logic and PID control approaches

Recent years have experienced a notable surge in unmanned aerial vehicles (UAV) research, prompting exploration into innovative concepts. This paper introduces a compact UAV harnessing the Coandă effect, an underexplored phenomenon in fluid mechanics. Featuring a single lift motor and two types of flaps, this UAV offers exceptional maneuverability, presenting significant challenges compared to conventional multi-rotor UAVs. To address these challenges, we explore the theoretical study, mechatronic design, and manufacturing complexities of the Coandă UAV. Emphasizing the distinctiveness of our work, we assess a Fuzzy Logic Controller (FLC) for UAV stabilization, marking the first application of such techniques to a Coandă-effect UAV, in contrast to the Proportional-Integral-Derivative (PID) control employed by other researchers. This innovative application of Fuzzy logic, particularly the Sugeno model, proves advantageous, offering faster and more robust control in uncertain or noisy environments. The proposed FLC strategy is systematically compared against a classical PID control approach, formulated based on the Mamdani and Sugeno models, optimized and manually tuned using a genetic algorithm. Our results showcase a significantly improved settling time of 0.417 s with the FLC strategy, surpassing the PID control approach by 35.23%. To substantiate our findings, we present comprehensive experimentation conducted at both software and hardware levels using Matlab® and Simulink for a microcontroller-based UAV. This groundbreaking fusion of novel design and advanced control techniques not only addresses the unique challenges posed by the Coandă UAV's aerodynamics but also contributes significantly to the field of UAV research.

Achieving stable trajectory tracking in complex environments using an adaptive PID control strategy-based quadcopter drone

Stable trajectory tracking of unmanned aerial vehicles (UAVs) in complex environments is of paramount importance for achieving high precision and robustness in flight missions. In this paper, we address the attitude control problem of quadrotor UAVs and propose an optimization method based on adaptive PID control strategy. This text first presents an overview of the current status of UAVs both domestically and internationally, followed by the establishment of a mathematical model for quadrotor UAVs. Next, analysing the application of the traditional PID algorithm in UAV attitude control and provide a detailed description of the principles behind genetic algorithms and simulated annealing algorithms, along with their application in optimizing PID parameters. Through simulation experiments conducted in strong wind conditions, we compare the performance of the traditional PID algorithm with the optimization algorithm in stable trajectory tracking tasks. The experimental results demonstrate that the optimization algorithm significantly enhances the flight stability and accuracy of UAVs. Finally, this text summarizes the research findings and provide an outlook on future development directions.

Adaptive Control of Unmanned Aerial Vehicles with Varying Payload and Full Parametric Uncertainties

This article investigates an adaptive tracking control problem for a six degrees of freedom (6-DOF) nonlinear quadrotor unmanned aerial vehicle (UAV) with a variable payload mass. The changing payload introduces time-varying parametric uncertainties into the dynamical model, rendering a static control strategy no longer effective. To handle this issue, two adaptive schemes are developed to maintain the uncertainties in the translational and rotational dynamics. Initially, a virtual proportional derivative (PD) is designed to stabilize the horizontal position; however, due to an unknown and time-varying mass, an adaptive controller is proposed to generate the total thrust of the UAV. Furthermore, an adaptive controller is designed for the rotational dynamics, to handle parametric uncertainties, such as inertia and external disturbance parameters. In both schemes, a standard adaptive scheme using the certainty equivalence principle is extended and designed. A stability analysis was conducted with rigorous analytical proofs to show the performance of our proposed controllers, and simulations were implemented to assess the performance against other existing methods. Tracking fitness and total control efforts were calculated and compared with closed-loop adaptive tracking control (CLATC) and adaptive sliding mode control (ASMC). The results indicated that the proposed design better maintained UAV stability.

OoD-Control: Generalizing Control in Unseen Environments.

Generalizing out-of-distribution (OoD) is critical but challenging in real applications such as unmanned aerial vehicle (UAV) flight control. Previous machine learning-based control has shown promise in dealing with complex real-world environments but suffers huge performance degradation facing OoD scenarios, posing risks to the stability and safety of UAVs. In this paper, we found that the introduced random noises during training surprisingly yield theoretically guaranteed performances via a proposed functional optimization framework. More encouragingly, this framework does not involve common Lyapunov assumptions used in this field, making it more widely applicable. With this framework, the upperbound for control error is induced. We also proved that the induced random noises can lead to lower OoD control errors. Based on our theoretical analysis, we further propose OoD-Control to generalize control in unseen environments. Numerical experiments demonstrate the superiority of the proposed algorithm, surpassing previous state-of-the-art by 65% under challenging unseen environments. We further extend to outdoor real-world experiments and found that the control error is reduced by 50% approximately. Our code is available athttps://github.com/Ulricall/OoD-Control.

Enhanced stability of aerial refueling process of UAV based on fuzzy PID

This essay aims to study UAV(Unmanned Aerial Vehicle) aerial refueling technology, and by understanding the control analysis of aerial UAV refueling by comparing multiple R&D(Research And Development) teams, a more practical and feasible method is obtained, that is, through the discussion and design of UAV motion modeling, UAV trajectory tracking control system, fuzzy PID(proportion integral derivative) controller design, etc., the purpose of UAV aerial refueling is finally achieved, and the time of UAV is increased to enhance the stability and durability of UAV in air operation.

Multi-Agent Bifurcation Consensus-Based Multi-Layer UAVs Formation Keeping Control and Its Visual Simulation

This paper focuses on the mult-layer unmanned aerial vehicles (UAVs) formation keeping control issue based on multi-agent bifurcation consensus. Taking the two-layer formation as an example, due to delays in receiving and responding to signals, the intra-layer and inter-layer control schemes under the dynamic properties need to be considered. Firstly, the UAV heading autopilot is designed to fully consider the control of the angle of attack and yaw angle, which is utilized to deal with the translational and angular motion of UAV. Based on the heading autopilot, the stability and Hopf bifurcation conditions of UAVs heading dynamic system are discussed to get real-time state information and generate control input. Secondly, the leader-following formation combined with the virtual control structure to obtain a virtual hierarchical control strategy, and then a bifurcation consensus criterion. The results shown that when UAVs heading control system achieves bifurcation consensus. The formation maintains the shape while showing interesting periodic oscillation motion behavior. Finally, a numerical example is presented to demonstrate the effectiveness of the presented control strategy. Particularly, visualization simulation using 3ds Max software makes the results more intuitive and flexible.

Control of a Novel Parallel Mechanism for the Stabilization of Unmanned Aerial Vehicles

The use of delivery drones is currently hindered by the inability of transported objects to maintain a steady position, which can result from roll-, pitch-, and heave-induced vibrations. This paper proposes a novel parallel manipulator for stabilizing the platform of unmanned aerial vehicles. The proposed mechanism builds upon an existing study of a 3-SRR/SRU parallel stabilizing mechanism by incorporating the dynamical properties of the system into the control model. The resultant control technique is then applied to both the 3-RRS and 3-SRR mechanisms, and a comparative study is conducted to identify the most reliable stabilizer for regulating the platform’s orientation. The results demonstrate that the 3-SRR mechanism exhibits superior robustness and stability characteristics compared to the other two mechanisms. Additionally, the 3-SRR mechanism is controlled using artificial neural networks, which significantly improves the accuracy and stability of the system. Overall, this research presents a novel and effective solution for stabilizing the platform of unmanned aerial vehicles, with significant implications for the development of delivery drone technology.

Computational simulation study on disturbance of six-rotor UAVs due to ammunition launch

According to the multi-body dynamics theory, we analyzed and derived the influence laws of the ammunition launch angle, launch timing, mounting position, and mass on the stability of unmanned aerial vehicles (UAVs) by establishing a flight dynamics model of a six-rotor UAV in the process of ammunition launch. The analyses indicated that the ammunition launch angle, the longitudinal mounting position, and the multi-round ammunition launch timing significantly affect the disturbance of the UAV. When single-round ammunition is launched, selecting the appropriate ammunition mounting position can reduce the disturbance amplitude of the position of the UAV, and when multi-round ammunition is launched, the launch timing significantly affects the overall stability of the UAV. On the basis of the overall system and ammunition-related parameters, the optimal launch timing for different deployment methods can be derived. Through modeling analysis, we can qualitatively study the stability of the system during ammunition launch, providing a reference for the design of the ammunition launcher structure and the strike timing.

QoS-Oriented Sensing–Communication–Control Co-Design for UAV-Enabled Positioning

Unmanned aerial vehicle (UAV)-enabled positioning that uses UAVs as aerial anchor nodes has been envisioned as a promising solution for providing positioning services in harsh environments. In previous research, state sensing and control of UAVs was either ignored or assumed to be performed continuously, resulting in system instability or a waste of wireless resources. Therefore, in this paper, we propose a quality-of-service (QoS)-oriented sensing-communication-control (SCC) co-design scheme for UAV-enabled positioning systems. We first establish mathematical models of UAV state sensing and control. Then, we analyze the influence of sensing scheduling and transmission failure on the stability of UAV, as well as the performance of positioning services in the presence of UAV control error. Based on these models and analysis results, we further study the problem of minimizing the amount of data transmitted by optimizing the sensing scheduling and blocklength allocation under the condition of meeting each user’s requirement on position accuracy. Finally, a heuristic algorithm is developed to solve this mixed-integer nonlinear problem. Numerical results demonstrate the validity and superiority of the proposed scheme. Compared with two benchmark schemes, our scheme reduces the failure rate or resource consumption of positioning services by more than 75% or 80%.

Application of CFD Numerical Simulation Methods in the Field of UAV Research

Unmanned aerial vehicle (UAV) has become one of the hot topics in today’s research. A drone’s short life has always been one of the main drawbacks, and fluid mechanics is related to the development of computer technology. CFD was used to simulate the aerodynamic parameters of UAVs and has become a more economical choice, which can greatly save the cost of materials. Solid-liquid coupling calculation generally chooses the SST k - Ω model to calculate and will also include an energy equation in the calculation. Then, according to the precision needs to choose a different order of upwind format. Choose either steady state or transient simulation according to different needs. CFD is widely used in research on the overall stability and reliability of UAVs and the experiment on the aerodynamic characteristics of UAV components.

Load and Stability Optimization Based Carbon Arm with Split Tank on Unmanned Aerial Vehicle for Foliar Fertilizer

This research focuses on the load and stability of unmanned aerial vehicles that are as light as possible but with optimal stability functions and increasing the capacity of liquid fertilizer lifted by unmanned aerial vehicles. The increase in lift is related to the payload and stability to be carried by the unmanned aerial vehicle. By optimizing the arm using the arm carbon method, so without reducing the strength and function of the drone, the gross weight of the components becomes much lighter, so that the weight can be focused on increasing the capacity of liquid fertilizer. With the increase in liquid fertilizer capacity, of course, the weight also increases, this raises a new problem, which is to make the balance value when the drone take-off has a large magnitude, thus affecting stability which causes the potential to fail to fly, therefore this research was carried out so that the drone has stability. maximum and can minimize the effects of shocks from increasing capacity after using arm-carbon. The measurements taken were the success rate of the system in flying over agricultural land, power testing and current measurements. After testing, it was found that by using a split tank and carbon arm system, current and power tend to be more stable, so that battery life is longer, liquid fertilizer capacity can be increased and the potential for flight failure can be minimized with a split tank system with 90% accuracy, carried out optimizing weight with carbon arms and increasing the capacity of liquid fertilizer by 30%, using a split tank system increases the stability of the drone at take-off by up to 80%.

Integer Ambiguity Parameter Identification for Fast Satellite Positioning and Navigation Based on LAMBDA-GWO with Tikhonov Regularization

Satellite positioning is one of the main navigation technologies in unmanned aerial vehicles (UAVs), the accuracy of which has an important impact on the safety, stability, and flexibility of UAVs. The parameters of integer ambiguity are important factors affecting the accuracy of satellite positioning. However, the accuracy of the integer ambiguity cannot be guaranteed when only a few epoch data can be obtained in the fast positioning such that the identification matrix of the integer ambiguity parameters is seriously ill-conditioned and the information of position deviation is enlarged. In this paper, an error checking and correcting strategy is proposed, where a Least-square Ambiguity Decorrelation Adjustment-Grey Wolf Optimization (LAMBDA-GWO) Method combined with the Tikhonov regularization method is developed to improve the accuracy of integer ambiguity for fast satellite positioning. More specifically, the LAMBDA-GWO is first used to search the integer ambiguity parameters. To reduce the ill-condition of the integer ambiguity parameter identification matrix, the Tikhonov regularization method is introduced to regularize the identification matrix such that a reliable integer ambiguity floating-point solution can be obtained. Furthermore, the correctness of the integer ambiguity is checked according to the prior accuracy information of the initial coordinates and the Total Electron Content (TEC), and the part that fails the test is corrected by the Grey Wolf Optimization (GWO) Method. Finally, experimental studies based on a 522 m baseline and a 975 m baseline show that the identification success rates of the proposed method are both above 99%, which is 12% and 23% higher than that of traditional LAMBDA, respectively.

Incidence Control Units Selection Scheme to Enhance the Stability of Multiple UAVs Network

The autonomous cooperation of multiple unmanned aerial vehicles (UAVs) will effectively improve the efficiency of task completion and meet the needs of task diversity. The information interaction and mutual control between UAVs restrict the autonomy of multi-UAVs. The number and selection of control units not only affect the equipment cost of the network but also affect the stability of the network. Link information, such as bandwidth resources and the computing power of the control unit, also affect the resource adjustment of other related links. In order to improve the efficiency and stability of UAVs computing first network, considering the number of control units and link information, this article creatively puts forward the concepts of incidence control, sign incidence control, and incidence complete control. The minimum incidence control number and incidence complete control number of general graphs are calculated. When the network topology of UAV meets the requirements of path, circle, and star, the exact value of the sign incidence control number is calculated. When the network topology of UAV meets the requirements of the complete graph, complete bipartite graph, and wheel graph, the bounds of the sign incidence control number are calculated. The number of control units of the UAV network is further determined.

Cooperative Path Optimization for Multiple UAVs Surveillance in Uncertain Environment

Research on multiple unmanned aerial vehicles (UAVs) cooperative surveillance systems serving Internet of Things (IoT) applications, such as smart cities, precision logistics, etc., has become a hot topic. However, the target movement is unpredictable in an uncertain environment, and multiple UAVs are affected by obstacles or inaccessible regions, resulting in the decreased surveillance performance and even the loss of the target. This article is dedicated to determine the current surveillance environment through the 2-D laser scanner. At the cost of the energy consumption and the transmission unreliability, a multi-UAV cooperative path optimization (MCPO) model is designed to adjust the surveillance location of each UAV, which improves the target surveillance performance. Specifically, for different types of obstacles or inaccessible regions, we present a novel obstacle-avoidance selection strategy with two mechanisms in mind: 1) when some of UAVs encounter obstacles, but others can accurately monitor the target, a strict constraint mechanism is established to promptly adjust the surveillance location of each UAV, which ensures the accuracy of formation surveillance and 2) when all UAVs have to avoid obstacles, a fuzzy constraint mechanism is presented and combined with Lucas–Kanade (LK) method to expand the search range of the multi-UAV and enhance the flexible adjustment capability of the formation. To verify the superiority of the proposed optimization method, we develop a 3-D simulation experiment environment based on the UE4 platform and design several groups of experiments to analyze the effectiveness of MCPO. The experimental results demonstrate that MCPO can not only maintain the flight stability of multiple UAVs but also has satisfactory formation flexibility and surveillance accuracy.