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Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV

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A special issue of Drones (ISSN 2504-446X).

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 22930

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Special Issue Editors

Prof. Dr. Kai Liu

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Guest Editor

School of Aeronautics and Astronautics, Dalian University of Technology, Dalian 116024, China
Interests: intelligent learning flight control technology; UAV flight control system research and development
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yongji Wang

E-Mail Website
Guest Editor

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: UAV guidance control; intelligent optimization and intelligent control

Assoc. Prof. Dr. Jia Song

E-Mail Website
Guest Editor

School of Astronautics, Beihang University, Beijing 100191, China
Interests: autonomous fault diagnosis based on hybrid intelligence; disturbance rejection and fault-tolerant guidance control for unmanned aerial vehicle; cooperative control of multi-agent based on hybrid intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Lei Liu

E-Mail Website
Guest Editor

School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
Interests: UAV navigation, guidance and control

Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit original manuscripts to the Special Issue of the MDPI journal Drones on “Aerodynamic parameter identification, actuator fault diagnosis and intelligent control of UAV”.

The actuator failures during the flight of the UAV will lead to the decline of the attitude stabilization control ability, reducing the reliability of the UAV system and imperiling flight safety. Although many scholars have carried out substantial research work on the fault-tolerant control of aircraft, the problem of rapid diagnosis and fault-tolerant control of sudden faults during the UAV flight has not been well solved. A promising way to solve this problem is the combination of fault diagnosis, online aerodynamic identification, and intelligent flight control, which is required to be fast and reliable. The Special Issue is intended to present an overview of the latest advances in UAV fault diagnosis, online aerodynamic identification, and intelligent flight control. The Special Issue expects to provide some worthful contributions to the research on fault diagnosis and autonomous learning control in the case of aircraft failures.

Potential topics include, but are not limited to:

Actuator failure;
Aircraft attitude control;
Robust system;
Adaptive control;
System identification;
Resilient control;
Intelligent control;
Fault-tolerant system;
Aerodynamics characteristics of UAVs;
Self-learning systems.

Prof. Dr. Kai Liu
Prof. Dr. Yongji Wang
Dr. Jia Song
Dr. Lei Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Drones is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

UAVs
online identification
reconfigurable control
resilient control
fault diagnosis
intelligent control
active fault tolerance

Published Papers (10 papers)

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Research

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24 pages, 10291 KiB

Open AccessFeature PaperArticle

On the Fidelity of RANS-Based Turbulence Models in Modeling the Laminar Separation Bubble and Ice-Induced Separation Bubble at Low Reynolds Numbers on Unmanned Aerial Vehicle Airfoil

by Manaf Muhammed and Muhammad Shakeel Virk

Drones 2024, 8(4), 148; https://doi.org/10.3390/drones8040148 - 09 Apr 2024

Viewed by 605

Abstract

The operational regime of Unmanned Aerial Vehicles (UAVs) is distinguished by the dominance of laminar flow and the flow field is characterized by the appearance of Laminar Separation Bubbles (LSBs). Ice accretion on the leading side of the airfoil leads to the formation of an Ice-induced Separation Bubble (ISB). These separation bubbles have a considerable influence on the pressure, heat flux, and shear stress distribution on the surface of airfoils and can affect the prediction of aerodynamic coefficients. Therefore, it is necessary to capture these separation bubbles in the numerical simulations. Previous studies have shown that these bubbles can be modeled successfully using the Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) but are computationally costly. Also, for numerical modeling of ice accretion, the flow field needs to be recomputed at specific intervals, thus making LES and DNS unsuitable for ice accretion simulations. Thus, it is necessary to come up with a Reynolds-Averaged Navier–Stokes (RANS) equation-based model that can predict the LSBs and ISBs as accurately as possible. Numerical studies were performed to assess the fidelity of various RANS turbulence models in predicting LSBs and ISBs. The findings are compared with the experimental and LES data available in the literature. The structure of these bubbles is only studied from a pressure coefficient perspective, so an attempt is made in these studies to explain it using the skin friction coefficient distribution. The results indicate the importance of the use of transition-based models when dealing with low-Reynolds-number applications that involve LSB. ISB can be predicted by conventional RANS models but are subjected to high levels of uncertainty. Possible recommendations were made with respect to turbulence models when dealing with flows involving LSBs and ISBs, especially for ice accretion simulations. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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23 pages, 13901 KiB

Open AccessArticle

Analysis of the Impact of Structural Parameter Changes on the Overall Aerodynamic Characteristics of Ducted UAVs

by Huarui Xv, Lei Zhao, Mingjian Wu, Kun Liu, Hongyue Zhang and Zhilin Wu

Drones 2023, 7(12), 702; https://doi.org/10.3390/drones7120702 - 11 Dec 2023

Viewed by 1521

Abstract

Ducted UAVs have attracted much attention because the duct structure can reduce the propeller tip vortices and thus increase the effective lift area of the lower propeller. This paper investigates the effects of parameters on the aerodynamic characteristics of ducted UAVs, such as co-axial twin propeller configuration and duct structure. The aerodynamic characteristics of the UAV were analyzed using CFD methods, while the impact sensitivity analysis of the simulation data was sorted using the orthogonal test method. The results indicate that, while maintaining overall strength, increasing the propeller spacing by about 0.055 times the duct chord length can increase the lift of the upper propeller by approximately 1.3% faster. Reducing the distance between the propeller and the top surface of the duct by about 0.5 times the duct chord length can increase the lift of the lower propeller by approximately 7.7%. Increasing the chord length of the duct cross-section by about 35.3% can simultaneously make the structure of the duct and the total lift of the drone faster by approximately 150.6% and 15.7%, respectively. This research provides valuable guidance and reference for the subsequent overall design of ducted UAVs. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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20 pages, 3062 KiB

Open AccessArticle

Time-Domain Identification Method Based on Data-Driven Intelligent Correction of Aerodynamic Parameters of Fixed-Wing UAV

by Dapeng Yang, Jianwen Zang, Jun Liu and Kai Liu

Drones 2023, 7(9), 594; https://doi.org/10.3390/drones7090594 - 21 Sep 2023

Viewed by 1003

Abstract

In order to overcome the influence of complex environmental disturbance factors such as nonlinear time-varying characteristics on the dynamic control performance of small fixed-wing UAVs, the nonlinear expression relationship of neural networks (NNs) is combined with the recursive least squares (RLSs) identification algorithm. This paper proposes a hybrid aerodynamic parameter identification method based on NN-RLS offline network training and online learning correction. The simulation results show that compared with the real value of the identification value obtained by this algorithm, the residual error of the moment coefficient is reduced by 69%, and the residual error of the force coefficient is reduced by 89%. Under the same identification accuracy, the identification time is shortened from the original 0.1 s to 0.01 s. Compared with traditional identification algorithms, better estimation results can be obtained. By using this algorithm to continuously update the NN model and iterate repeatedly, iterative learning for complex dynamic models can be realized, providing support for the optimization of UAV control schemes. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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21 pages, 4830 KiB

Open AccessArticle

A Modified Model-Free Adaptive Control Method for Large-Scale Morphing Unmanned Vehicles

by Haohui Che, Jun Chen, Guanghui Bai and Jianying Wang

Drones 2023, 7(8), 495; https://doi.org/10.3390/drones7080495 - 27 Jul 2023

Viewed by 772

Abstract

This paper investigates the attitude control problem for large-scale morphing unmanned vehicles. Considering the rapid time-varying and strong aerodynamic interference caused by large-scale morphing, a modified model-free control method utilizing only the system input and output is proposed. Firstly, a two-loop equivalent data model for the morphing unmanned vehicle is developed, which can better reflect the practical dynamics of morphing unmanned vehicles compared to the traditional compact form dynamic linearization data model. Based on the proposed data model, a modified model-free adaptive control (MMFAC) scheme is proposed, consisting of an external-loop and an inner-loop controller, so as to generate the required combined control torques. Additionally, in light of the aerodynamic uncertainties of the large-scale morphing unmanned vehicle, a rudder deflection actuator control scheme is designed by employing the model-free adaptive control approach. Finally, the boundedness of the closed-loop system and the convergence of tracking errors are guaranteed, based on the stability analysis. Additionally, numerical examples are presented to demonstrate the effectiveness and robustness of the proposed control scheme in the case of the effect of large-scale morphing. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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24 pages, 7472 KiB

Open AccessArticle

Research on Direct Lift Carrier-Based Unmanned Aerial Vehicle Landing Control Based on Performance Index Intelligent Optimization/Dynamic Optimal Allocation

by Dapeng Zhou and Lixin Wang

Drones 2023, 7(7), 431; https://doi.org/10.3390/drones7070431 - 28 Jun 2023

Viewed by 838

Abstract

High-precision control problems for carrier-based UAVs are challenging due to the requirements for safety, high-performance operation and uncertain ocean environments. To address such problems, this paper proposes a direct lift landing control method that can ensure the safe operation of UAVs using intelligent optimization of control performance indicators and dynamic optimal allocation methods. The direct lift control (DLC) scheme is adopted to improve the operability of the control by introducing flap channels to achieve the fast correction of vertical disturbances. To improve the landing control performance, an intelligent optimization method for DLC gain is proposed, and a neural network relationship between parameter uncertainty and optimal DLC gain is established. In addition, a recursive least-squares identification method is used to estimate the uncertain parameters in real time, so that the established neural network can be used to generate the optimal DLC commands online, and the generated control commands can be assigned to multiple actuators of the carrier-based UAV through a dynamic optimal assignment algorithm. Finally, the superiority of the method is verified using simulation comparison. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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22 pages, 2783 KiB

Open AccessArticle

Attitude Control of a Hypersonic Glide Vehicle Based on Reduced-Order Modeling and NESO-Assisted Backstepping Variable Structure Control

by Wenxin Le, Hanyu Liu, Ruiyuan Zhao and Jian Chen

Drones 2023, 7(2), 119; https://doi.org/10.3390/drones7020119 - 08 Feb 2023

Cited by 1 | Viewed by 1537

Abstract

Aiming at solving the control problem caused by the large-scale change of the Hypersonic Glide Vehicle (HGV) parameters, this paper proposes a design method of backstepping variable structure attitude controller based on Nonlinear Extended State Observer (NESO), with the characteristics of HGV model and the idea of uncertainty estimation and compensation associated. Firstly, the design of the second-order NESO is studied. Due to the large number of NESO parameters, a systematic method for determining the second-order NESO parameters is given in this paper, and the stability of the observer is proved completely using the piecewise Lyapunov analysis. Then, the NESO-assisted backstepping variable structure attitude controller employs the reduced-order modeling idea to decompose the whole system design problem into two first-order subsystem design problem, and classifies the nonlinear dynamic changes caused by the large-scale changes of the aircraft parameters into the aggregated uncertain terms of the two subsystems. The simulation results show that the backstepping attitude controller based on NESO can realize the stable and accurate tracking of the flight attitude when the aircraft parameters change in a large range. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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18 pages, 4596 KiB

Open AccessArticle

Research on Adaptive Prescribed Performance Control Method Based on Online Aerodynamics Identification

by Shuaibin An, Jianwen Zang, Ming Yan, Baiyang Zhu and Jun Liu

Drones 2023, 7(1), 50; https://doi.org/10.3390/drones7010050 - 11 Jan 2023

Cited by 1 | Viewed by 1299

Abstract

Wide-speed-range vehicles are characterized by high flight altitude and high speed, with significant changes in the flight environment. Due to the strong uncertainty of its aerodynamic characteristics, higher requirements are imposed on attitude control. In this paper, an adaptive prescribed performance control method based on online aerodynamic identification is proposed, which consists of two parts: an online aerodynamic parameter identification method and an adaptive attitude control method based on the pre-defined parameters of the control system. The aerodynamic parameter identification is divided into offline design and online design. In the offline design, neural networks are used to fit nonlinear aerodynamic characteristics. In the online design, a nonlinear recursive identification method is used to correct the errors of the offline fitted model. The adaptive attitude control is based on the conventional control method and updates the control gain in real time according to the desired system parameters to enhance the robustness of the controller. Finally, the effectiveness of the offline neural network and online discrimination correction is verified by mathematical simulations, and the effectiveness and robustness of the adaptive control proposed in this paper are verified by comparative simulation. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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20 pages, 3436 KiB

Open AccessArticle

Deep Learning-Based Real Time Defect Detection for Optimization of Aircraft Manufacturing and Control Performance

by Imran Shafi, Muhammad Fawad Mazhar, Anum Fatima, Roberto Marcelo Alvarez, Yini Miró, Julio César Martínez Espinosa and Imran Ashraf

Drones 2023, 7(1), 31; https://doi.org/10.3390/drones7010031 - 01 Jan 2023

Cited by 9 | Viewed by 3439

Abstract

Monitoring tool conditions and sub-assemblies before final integration is essential to reducing processing failures and improving production quality for manufacturing setups. This research study proposes a real-time deep learning-based framework for identifying faulty components due to malfunctioning at different manufacturing stages in the aerospace industry. It uses a convolutional neural network (CNN) to recognize and classify intermediate abnormal states in a single manufacturing process. The manufacturing process for aircraft factory products comprises different phases; analyzing the components after the integration is labor-intensive and time-consuming, which often puts the company’s stake at high risk. To overcome these challenges, the proposed AI-based system can perform inspection and defect detection and alleviate the probability of components’ needing to be re-manufacturing after being assembled. In addition, it analyses the impact value, i.e., rework delays and costs, of manufacturing processes using a statistical process control tool on real-time data for various manufactured components. Defects are detected and classified using the CNN and teachable machine in the single manufacturing process during the initial stage prior to assembling the components. The results show the significance of the proposed approach in improving operational cost management and reducing rework-induced delays. Ground tests are conducted to calculate the impact value followed by the air tests of the final assembled aircraft. The statistical results indicate a 52.88% and 34.32% reduction in time delays and total cost, respectively. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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18 pages, 7241 KiB

Open AccessArticle

Fractional-Order Linear Active Disturbance Rejection Control Design and Optimization Based Improved Sparrow Search Algorithm for Quadrotor UAV with System Uncertainties and External Disturbance

by Jia Song, Yunlong Hu, Jiangcheng Su, Mingfei Zhao and Shaojie Ai

Drones 2022, 6(9), 229; https://doi.org/10.3390/drones6090229 - 31 Aug 2022

Cited by 9 | Viewed by 1742

Abstract

This paper presents a generic robust controller that allows applications in various quadrotor unmanned aerial vehicle (UAV) systems effectively even when facing severe system uncertainties and unknown external disturbances. The fractional-order linear active disturbance rejection control (FOLADRC) scheme has combined the advantages of the fractional-order PID (FOPID) with the linear active disturbance rejection control (LADRC). Firstly, the structure of the FOLADRC-based quadrotor UAV is designed. Then, considering the difficulty of parameter tuning of FOLADRC and the demand for accuracy and rapidity of the controller, the improved sparrow search algorithm is applied. Finally, to illustrate the robustness and effectiveness of FOLADRC, the FOLADRC-based quadrotor UAV is firstly compared with PID and LADRC. The simulation and experiment results show that the FOLADRC method can suppress the influence of system uncertainties and external disturbance effectively, where the superiority compared to PID and LADRC has been demonstrated clearly. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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Review

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39 pages, 2136 KiB

Open AccessReview

UAV Fault Detection Methods, State-of-the-Art

by Radosław Puchalski and Wojciech Giernacki

Drones 2022, 6(11), 330; https://doi.org/10.3390/drones6110330 - 29 Oct 2022

Cited by 29 | Viewed by 8905

Abstract

The continual expansion of the range of applications for unmanned aerial vehicles (UAVs) is resulting in the development of more and more sophisticated systems. The greater the complexity of the UAV, the greater the likelihood that a component will fail. Due to the fact that drones often operate in close proximity to humans, the reliability of flying robots, which directly affects the level of safety, is becoming more important. This review article presents recent research works on fault detection on unmanned flying systems. They include papers published between January 2016 and August 2022. Web of Science and Google Scholar databases were used to search for articles. Terminology related to fault detection of unmanned aerial vehicles was used as keywords. The articles were analyzed, each paper was briefly summarized and the most important details concerning each of the described articles were summarized in the table. Full article

(This article belongs to the Special Issue Aerodynamic Parameter Identification, Actuator Fault Diagnosis and Intelligent Control of UAV)

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