Quadrotor Unmanned Aerial Vehicle Research Articles

Robust Geometric Control for a Quadrotor UAV with Extended Kalman Filter Estimation

This study proposes a robust geometric controller tailored for quadrotor unmanned aerial vehicles (UAVs). The original geometric controller exhibits excellent performance in quadrotor UAV maneuvers. However, as a model-based nonlinear control method, it is sensitive to system model parameters. By integrating a novel extended Kalman filter (EKF)-based estimator for real-time, online estimation of the quadrotor’s inertia parameters, the controller adeptly handles internal uncertainties and external perturbations during flight maneuvers. This approach significantly improves the robustness of the control system against model inaccuracies. Empirical evidence is provided through both simulation and extensive real-world flight tests, demonstrating the controller’s effectiveness and its practical applicability in dynamic environments. The results confirm that this integration substantially enhances system reliability and performance under varied operational conditions.

Formation Control of Quadrotor UAVs

In recent years, the rapid development of quadrotor unmanned aerial vehicles (UAVs) has resulted in their widespread applications across various scenarios. However, single UAVs encounter multiple challenges during mission execution. Consequently, an increasing number of tasks are being conducted in the form of UAV formations. This paper focuses on quadrotor UAVs and proposes a formation control scheme that can be validated through simulation and experimentation. The research begins with the dynamic modeling of quadrotor UAVs and the design of an experimental platform for UAVs. The formation structure of UAVs is then developed based on the Leader-Follower theory, and a multi-type PID controller is employed to design the formation control algorithm.

Disturbance observer–based finite‐time fault tolerant control of quadrotor unmanned aerial vehicles with command filtered and event‐triggered techniques

AbstractThis article primarily focuses on the finite‐time fault‐tolerant tracking control problem of quadrotor unmanned aerial vehicle (UAV) systems. Our main innovation points are: (1) A simple cubic form of Lyapunov function is applied to the design of the position ring controller of the quadrotor UAV; (2) The command filtering technology effectively solves the challenge of derivation of complex coupling matrixs in the controller design process; (3) An improved disturbance observer and a variable threshold event‐triggered mechanism are applied in the Quadrotor UAV system. In summary, the overall approach combines the backstepping method and finite‐time Lyapunov stability to prove the finite‐time stability of all the signals in the system. Finally, the effectiveness and rationality of the algorithm are verified through simulation experiments and its application on an actual simulation platform.

Tracking controller design for quadrotor UAVs under external disturbances using a high-order sliding mode-assisted disturbance observer

Quadrotor unmanned aerial vehicles (UAVs) operating in agricultural fields for aerial photography are susceptible to external disturbances. The disturbances result in trajectory deviation and irregular image overlapping that considerably degrade image quality. Disturbance observers (DOs) are commonly researched for counteracting these effects but may have delays and limitations in handling diverse and high-frequency disturbances. To this end, this work proposes a continuous high-order sliding mode-assisted DO (HSMDO) with limited time convergence characteristics for the estimation of disturbances in systems. The observer consists of a classical nonlinear DO (NDO) and a sliding mode-assisted system (SMAS). The NDO is used to estimate disturbances preliminarily. The SMAS is utilised to assist the NDO in estimating the high-frequency component of disturbances and ensure that the entire DO is finite-time convergent. Finally, the tracking controller is designed on the basis of the HSMDO, which enables UAVs to track the prescribed trajectories under disturbances stably. Simulation results show that the proposed HSMDO can accurately estimate various types of disturbances. Moreover, the tracking controller based on the HSMDO can improve the antidisturbance performance of systems and ensure the trajectory tracking accuracy of UAVs.

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.

A novel quadrotor carrying payload concept via PID with Feedforward terms

PurposeThis paper introduces control strategy to enhance the performance of a novel quadrotor unmanned aerial vehicle designed for medical payload delivery. The aim is to achieve precise control and stability when carrying and releasing payloads, which alter the quadrotor’s mass and inertia characteristics.Design/methodology/approachThe equations of motion specific to the payload-carrying quadrotor are derived. A feedforward-proportional-integral-derivative (FF-PID) control strategy is then proposed to address the dynamic changes during payload release. The PID components use propeller speed/orientation information for stability. FF terms based on derivatives of desired position/orientation variables enable adaptation to real-time mass fluctuations.FindingsExtensive simulations, encompassing various fault scenarios, substantiate the effectiveness of the FF-PID approach. Notably, our findings demonstrate superior performance in maintaining altitude precision and stability during critical phases such as takeoff, payload release and landing. Graphical representations of thrust and mass dynamics distinctly illustrate the payload release event. In contrast to the linear quadratic regulator (LQR) and conventional PID control, which encountered difficulties during the payload release process, our approach proves its robustness and reliability.Research limitations/implicationsThis study, primarily based on simulations, demands validation through real-world testing in diverse conditions. Uncertainties in dynamic parameters, external factors and the applicability of the proposed approach to other quadrotor configurations require further investigation. Additionally, this research focuses on controlled payload release, leaving unexplored the challenges posed by unforeseen scenarios or disturbances. Hence, adaptability and fault tolerance necessitate further exploration. While our work presents a promising approach, practical implementation, adaptability and resilience to unexpected events are vital considerations for future research in the field of autonomous aerial medical deliveries.Practical implicationsThe proposed control strategy promises enhanced efficiency, reliability and adaptability for autonomous aerial medical deliveries in critical scenarios.Social implicationsThe innovative control strategy introduced in this study holds the potential to significantly impact society by enhancing the reliability and adaptability of autonomous aerial medical deliveries. This could lead to faster and more efficient delivery of life-saving supplies to remote or disaster-affected areas, ultimately saving lives and reducing suffering. Moreover, the technology’s adaptability may have broader applications in fields like disaster relief, search and rescue missions, and industrial cargo transport. However, its successful integration into society will require careful regulation, privacy safeguards and ethical considerations to ensure responsible and safe deployment while addressing potential concerns related to noise pollution and privacy intrusion.Originality/valueWhile PID control of quadrotors is extensively studied, payload release dynamics have been overlooked. This research studies integration of FF control to enable PID adaptation for a novel payload delivery application.

Fault diagnosis of drone motors driven by current signal data with few samples

Multi rotor unmanned aerial vehicles (UAVs) are extensively utilized across various domains, and the motor constitutes a pivotal element in the UAV power system. The majority of UAV failures and crashes stem from motor malfunctions, underscoring the imperative need for comprehensive research on fault diagnosis in UAV motors to ensure the stable and reliable execution of flight tasks. This study focuses on quadrotor UAVs as the research subject and devises targeted fault simulation experiments based on the structural features and operational characteristics of the DC brushless motor used in quadrotor UAVs, specifically examining the stator, rotor, and bearings. To address challenges related to the UAV’s own loads, limited space for redundant parts, and the high cost and difficulty associated with installing sensors for traditional fault diagnostic signals such as vibration and temperature, this study opts to use current signals as a substitute. This approach resolves the issue of challenging data collection for UAVs and investigates a current signal based fault diagnosis method for UAV motors. Lastly, in response to the limited training samples available for fault data due to the UAV’s highly sensitive characteristics regarding the health status of its components and flight stability, traditional machine learning and deep learning methods encounter difficulties in identifying representative features with a small number of training samples, leading to the risk of overfitting and reduced model accuracy in fault diagnosis. To overcome this challenge, we propose a hybrid neural network fault diagnosis model that incorporates a width learning system and a convolutional neural network (CNN). The width learning system eliminates temporal characteristics from the original current signal, capturing more comprehensive and representative sample features in the width feature space. Subsequently, the CNN is employed for feature extraction and classification tasks. In empirical small sample fault diagnosis experiments using current signal data for UAV motors, our proposed model outperforms other models used for comparison.

A Separation Modeling Method for Morphing QUAV: Analytical Solutions for Constraint Forces Under Deformation

Abstract A morphing quadrotor unmanned aerial vehicle (QUAV) possesses the remarkable ability to alter its shape, enabling it to navigate through gaps smaller than its wingspan. However, these deformations result in changes to the system's center of gravity and moment of inertia, necessitating real-time computation of each state's variations. To address this challenge, we propose a dynamic modeling approach based on the Udwadia−Kalaba (U-K) method. The morphing QUAV is divided into three separate subsystems, with the dynamic modeling for each subsystem conducted independently. Subsequently, the QUAV's deformation states and inherent structure are introduced in the form of constraints, and the constrained forces are derived using the U-K equation. By combining these analytical solutions, the model of the QUAV under continuous deformation is obtained. This approach effectively simplifies the modeling computations caused by changes in the system's center of gravity and moment of inertia during deformation. A control approach is proposed to achieve attitude stabilization and altitude control for the morphing QUAV. Ultimately, the stable motion of the morphing QUAV is validated through numerical simulations.

Tank-mix adjuvants improved spray performance and biological efficacy in rice insecticide application with unmanned aerial vehicle sprayer.

The use of unmanned aerial vehicles (UAVs) for the application of plant protection products (PPPs) in paddy fields is becoming increasingly prevalent worldwide. Despite its growing usage, UAV spraying for rice pest control faces practical challenges, including limited canopy penetration, uneven deposition, and significant spray drift. This study investigated the impact of two tank-mix adjuvants, Wonderful Rosin (Adjuvant-1) and Tiandun (Adjuvant-2), at six volume concentrations, on the spray liquid's physicochemical properties, spray drift, plant deposition, and the biological efficacy of rice insecticides using a quadrotor UAV sprayer. The physicochemical characteristics of the spray liquid influenced spray performance and biological efficacy. Incorporating Adjuvant-1 and Adjuvant-2 led to a decrease in surface tension and contact angle while increasing the viscosity of the spray solution. These alterations in surface tension and viscosity contributed to an optimized droplet size distribution, reduced spray drift, enhanced deposition uniformity and penetration, and improved control efficacy against the rice planthopper in UAV applications. The highest control efficacy was observed at a concentration of 0.5%, showing an improvement of 35.12% (Adjuvant-1) and 20.23% (Adjuvant-2) over applications without tank-mix adjuvant 7 days after treatment. The judicious selection of tank-mix adjuvants for UAV PPP applications can significantly enhance spray performance and biological efficacy in controlling rice insects. This study's findings offer valuable insights for integrating tank-mix adjuvants into UAV spraying applications. © 2024 Society of Chemical Industry.

Distributed predefined-time estimator-based affine formation target-enclosing maneuver control for cooperative underactuated quadrotor UAVs with fault-tolerant capabilities

The paper presents a two-layer, disturbance-resistant, and fault-tolerant affine formation maneuver control scheme that accomplishes the surrounding of a dynamic target with multiple underactuated Quadrotor Unmanned Aerial Vehicles (QUAVs). This scheme mainly consists of predefined-time estimators and fixed-time tracking controllers, with a hybrid Laplacian matrix describing the communication among these QUAVs. At the first layer, we devise predefined time estimators for leading and following QUAVs, enabling accurate estimation of desired information. In the second layer, we initially devise a fixed-time hybrid observer to estimate unknown disturbances and actuator faults. Fixed-time translational tracking controllers are then proposed, and the intermediary control input from these controllers is used to extract the desired attitude and angular velocities for the fixed-time rotational tracking controllers. We employ an exact tracking differentiator to handle variables that are challenging to differentiate directly. The paper includes a demonstration of the control system stability through mathematical proof, as well as the presentation of simulation results and comparative simulations.

Quadrotor UAV Trajectory Tracking Control Based on Adaptive Non-singular Terminal Sliding Mode

To address the issue of the quadrotor unmanned aerial vehicle (UAV) being susceptible to external disturbances and model uncertainties during flight, an adaptive non-singular terminal sliding mode controller with disturbance compensation is proposed. Firstly, the quadrotor UAV dynamic model considering system disturbances is constructed using the Newton-Euler method. Next, the trajectory tracking problem is transformed into a command tracking control problem for each channel. To enhance the adaptability and robustness of trajectory tracking for the quadrotor UAV, an adaptive law is designed to dynamically adjust the sliding mode surface gain. In addition, a disturbance observer is introduced to estimate and compensate for external disturbances and model uncertainties. Finally, the stability of the designed controller is proven through the Lyapunov function. Simulation results demonstrate that the proposed controller effectively enhances the disturbance resistance and trajectory tracking response speed of the quadrotor UAV.

Finite-Time Attitude Control of Quadrotor Unmanned Aerial Vehicle with Disturbance and Actuator Saturation

This paper introduces a nonlinear dynamic inversion control algorithm designed to address unknown disturbances and actuator saturation issues in unmanned aerial vehicle (UAV) attitude control. The algorithm is based on a combination of finite-time disturbance observer and anti-saturation auxiliary system, which ensures the rapid convergence of attitude tracking error. Firstly, based on the Newton–Euler equations, this paper establishes a model of the attitude system for quadrotor UAVs, and this paper eliminates the small-angle flight assumption. Secondly, considering the actuator saturation problem, an anti-saturation auxiliary control system is designed to shorten the time when the control volume is in the saturation interval and achieve finite-time convergence of the attitude error. And then, to improve the robustness of the controller, this paper proposes a disturbance observer based on the finite-time stability theory, which achieves a continuous smooth output of the observation results by introducing a hyperbolic tangent function in the observer, so that the observation error can be converged to zero in a finite time. Finally, it is demonstrated by Simulink simulation that the attitude error and the observation error converge quickly to zero.

Nonlinear disturbance observer-based geometric attitude fault-tolerant control of quadrotors

This paper is concerned with nonlinear disturbance observer (DO)-based geometric attitude fault-tolerant control (FTC) problem of quadrotor unmanned aerial vehicles (UAVs) subject to system uncertainties, exogenous disturbances, and actuator faults. A disturbance observer-based control (DOBC) framework is devised to tackle the difficulties caused by system uncertainties and external disturbances. The FTC is proposed to cope with the occurrence of actuator faults which can be modelled as a constant loss of effectiveness. The proposed design scheme can provide an effective way to analyse the stability of the closed-loop system, which can ensure the asymptotical convergence of the error. The other method considers actuator failures as part of the lumped disturbance, and the attitude tracking problem of quadrotors concerning the lumped disturbance is solved by the DO-based control method, which can ensure the exponential convergence of the error. The numerical simulations are performed to illustrate the effectiveness of the design techniques.

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.

Fractional-Order Sliding Mode Observer for Actuator Fault Estimation in a Quadrotor UAV

In this paper, we present the design of a fractional-order sliding mode observer (FO-SMO) for actuator fault estimation in a quadrotor unmanned aerial vehicle (QUAV) system. Actuator faults can significantly compromise the stability and performance of QUAV systems; therefore, early detection and compensation are crucial. Sliding mode observers (SMOs) have recently demonstrated their accuracy in estimating faults in QUAV systems under matched uncertainties. However, existing SMOs encounter difficulties associated with chattering and sensitivity to initial conditions and noise. These challenges significantly impact the precision of fault estimation and may even render fault estimation impossible depending on the magnitude of the fault. To address these challenges, we propose a new fractional-order SMO structure based on the Caputo derivative definition. To demonstrate the effectiveness of the proposed FO-SMO in overcoming the limitations associated with classical SMOs, we assess the robustness of the FO-SMO under three distinct scenarios. First, we examined its performance in estimating actuator faults under varying initial conditions. Second, we evaluated its ability to handle significant chattering phenomena during fault estimation. Finally, we analyzed its performance in fault estimation under noisy conditions. For comparison purposes, we assess the performance of both observers using the Normalized Root-Mean-Square Error (NRMSE) criterion. The results demonstrate that our approach enables more accurate actuator fault estimation, particularly in scenarios involving chattering phenomena and noise. In contrast, the performance of classical (non-fractional) SMO suffers significantly under these conditions. We concluded that our FO-SMO is more robust to initial conditions, chattering phenomena, and noise than the classical SMO.

Distributed formation control for leader‐follower quadrotor unmanned aerial vehicles system based on practical sliding mode and event‐triggered mechanisms

AbstractThis article investigates the distributed formation trajectory tracking problem of a homogeneous quadrotor unmanned aerial vehicles (UAVs) system using a leader‐follower consensus protocol in three‐dimensional space. To enable the follower UAVs in the swarm to track a single leader UAV and form a desired formation under undirected topology, a six‐degree‐of‐freedom dynamic model with bounded time‐varying disturbances is established, which is divided into a tracking subsystem and a stabilizing subsystem. A practical sliding mode control (PSMC) based event‐triggered algorithm is proposed to achieve distributed formation control for a leader‐follower quadrotor UAVs system and reduce the network transmission resources. To address the potential issue of inter‐UAVs collisions during formation, an artificial potential field (APF) method is introduced in the tracking subsystem, and an disturbance observer is introduced in the stabilizing subsystem to estimate the unknown external disturbances. The boundedness of signals in the closed‐loop system is demonstrated using Lyapunov stability theory, and the exclusion of Zeno behavior can be ensured by having a positive lower bound on the inter‐event time between two consecutive triggering events. Finally, the effectiveness of the proposed event‐triggered algorithm is validated through simulation examples.

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

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

Quadrotor Modeling Approaches and Trajectory Tracking Control Algorithms: A Review

Quadrotor unmanned aerial vehicles are utilized in basically every sector of society, including the business, civil, and military industries. Popular applications include delivery, agriculture, target-acquisition, surveying, surveillance, and rescue. They are widely used due to their exceptional features such as accuracy, capability to perform swift inspections, simplicity in deploying perilous and uncertain missions, and additional praiseworthy attributes. This article presents a comprehensive analysis of the theoretical frameworks that have been proposed for the purpose of quadrotor modelling and control. Detailed examinations are conducted on every methodology that underpins the control algorithms, spanning from traditional linear to modern. The analysis looks at hybrid control technique models, which incorporate adaptive components across multiple controllers to improve overall performance and resilience by addressing individual algorithm shortcomings. This analysis also delves deeper into potential future research avenues. These include the development of learning-based or hybrid methodologies that employ machine learning and artificial intelligence to optimize performance and adaptability. For instance, model reference adaptive control systems can learn adaptation laws through machine learning techniques, as opposed to depending on predefined adaptation laws. By training neural networks or fuzzy logic controllers to forecast optimal adaptation parameters based on sensor data, the quadrotor can adjust to fluctuating conditions more effectively. A comparison table is provided to elaborate on the advantages, disadvantages, and hybrid versions of each control algorithm. This will serve as a concise guide that will promote innovation, facilitate the selection and integration of appropriate control algorithms, and enhance the functionality of quadrotor control systems.

Quadrotor attitude control by improved snake optimizer based adaptive switching disturbance rejection approach

In this paper, an adaptive switching anti-disturbance attitude control scheme based on improved snake optimizer (SO) is proposed for quadrotor attitude control when a quadrotor unmanned aerial vehicle is affected by measurement noise. The adaptive switching disturbance rejection controller (AWDRC) is composed of linear active disturbance rejection control and adaptive switching extended state observer which is used to achieve accurate signals reconstruction performance under measurement noise. Then, the improved SO (ISO) algorithm is developed with quadratic interpolation and comprehensive learning strategies to obtain the optimal parameters of the quadrotor attitude controller. The performance validity of ISO is demonstrated here by experiments on the CEC-2017 and the CEC-2020 benchmark functions with several state-of-the-art meta-heuristic algorithms. Secondly, the proposed ISO-based AWDRC algorithm is used in quadrotor attitude tracking control and compared with three other excellent active disturbance rejection controllers in a comparative experiment, and the experimental results show the effectiveness of the proposal. Finally, the robustness of the proposed method to parameters perturbation of the quadrotor attitude system is analyzed by Monte Carlo experiments.

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.