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Machines
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Advanced Control of Unmanned Aerial Vehicles (UAV)
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Advanced Control of Unmanned Aerial Vehicles (UAV)

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Vehicle Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 7405

Special Issue Editors

Dr. Chao Huang

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: human–machine collaborative control; decision making; path planning; fault-tolerant control with the application of automated vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Hailong Huang

E-Mail Website
Guest Editor
Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Interests: multiagent systems; distributed control; shared control; wireless sensor networks; UAV-based applications: search and rescue, construction automation, surveillance, wireless communications, parcel delivery, etc.
Special Issues, Collections and Topics in MDPI journals
Dr. Yang Xu

E-Mail Website
Guest Editor
School of Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Interests: control theory; application of multiple robotic systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, unmanned aerial vehicles (UAVs) have found great use in various applications, including, but not limited to, package delivery, surveillance, inspection, precision agriculture, border control, criminal investigations, search and rescue, weather measurement and forecasting, and disaster relief. The potential uses are remarkably diverse, and as UAV technology becomes more accessible, they are likely to continue to be used in new and surprising ways. Unlike military-grade products, most commercial UAVs are powered by an on-board battery extremely limited in capacity, and so can only fly for a short time (typically less than half an hour). This significantly limits the payload, which results in it not being able to carry too many sensors. This further creates challenges for the control of UAVs.

Dr. Chao Huang
Dr. Hailong Huang
Dr. Yang Xu
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. Machines 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 2400 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

  • safe and fast flight in cluttered/GPS-denied environments
  • tracking of adversarial moving targets
  • constrained motion planning and trajectory optimization
  • robust collaborative localization and mapping of UAVs
  • end-to-end deep-learning-based localization and mapping of UAVs
  • energy-efficient formation and coordination of UAV teams
  • edvanced control of UAV with nonlinear aerodynamics
  • disturbance rejection for UAV control
  • system identification for UAV

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Published Papers (3 papers)

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Research

30 pages, 11533 KiB  
Article
Application of UAVs and Image Processing for Riverbank Inspection
by Chang-Hsun Chiang and Jih-Gau Juang
Machines 2023, 11(9), 876; https://doi.org/10.3390/machines11090876 - 1 Sep 2023
Cited by 5 | Viewed by 1576
Abstract
Many rivers are polluted by trash and garbage that can affect the environment. Riverbank inspection usually relies on workers of the environmental protection office, but sometimes the places are unreachable. This study applies unmanned aerial vehicles (UAVs) to perform the inspection task, which [...] Read more.
Many rivers are polluted by trash and garbage that can affect the environment. Riverbank inspection usually relies on workers of the environmental protection office, but sometimes the places are unreachable. This study applies unmanned aerial vehicles (UAVs) to perform the inspection task, which can significantly relieve labor work. Two UAVs are used to cover a wide area of riverside and capture riverbank images. The images from different UAVs are stitched using the scale-invariant feature transform (SIFT) algorithm. Static and dynamic image stitching are tested. Different you only look once (YOLO) algorithms are applied to identify riverbank garbage. Modified YOLO algorithms improve the accuracy of riverine waste identification, while the SIFT algorithm stitches the images obtained from the UAV cameras. Then, the stitching results and garbage data are sent to a video streaming server, allowing government officials to check waste information from the real-time multi-camera stitching images. The UAVs utilize 4G communication to transmit the video stream to the server. The transmission distance is long enough for this study, and the reliability is excellent in the test fields that are covered by the 4G communication network. In the automatic reconnection mechanism, we set the timeout to 1.8 s. The UAVs will automatically reconnect to the video streaming server if the disconnection time exceeds the timeout. Based on the energy provided by the onboard battery, the UAV can be operated for 20 min in a mission. The UAV inspection distance along a preplanned path is about 1 km at a speed of 1 m/s. The proposed UAV system can replace inspection labor, successfully identify riverside garbage, and transmit the related information and location on the map to the ground control center in real time. Full article
(This article belongs to the Special Issue Advanced Control of Unmanned Aerial Vehicles (UAV))
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18 pages, 2851 KiB  
Article
Air Combat Maneuver Decision Method Based on A3C Deep Reinforcement Learning
by Zihao Fan, Yang Xu, Yuhang Kang and Delin Luo
Machines 2022, 10(11), 1033; https://doi.org/10.3390/machines10111033 - 5 Nov 2022
Cited by 18 | Viewed by 2949
Abstract
To solve the maneuvering decision problem in air combat of unmanned combat aircraft vehicles (UCAVs), in this paper, an autonomous maneuver decision method is proposed for a UCAV based on deep reinforcement learning. Firstly, the UCAV flight maneuver model and maneuver library of [...] Read more.
To solve the maneuvering decision problem in air combat of unmanned combat aircraft vehicles (UCAVs), in this paper, an autonomous maneuver decision method is proposed for a UCAV based on deep reinforcement learning. Firstly, the UCAV flight maneuver model and maneuver library of both opposing sides are established. Then, considering the different state transition effects of various actions when the pitch angles of the UCAVs are different, the 10 state variables including the pitch angle, are taken as the state space. Combined with the air combat situation threat assessment index model, a two-layer reward mechanism combining internal reward and sparse reward is designed as the evaluation basis of reinforcement learning. Then, the neural network model of the full connection layer is built according to an Asynchronous Advantage Actor–Critic (A3C) algorithm. In the way of multi-threading, our UCAV keeps interactively learning with the environment to train the model and gradually learns the optimal air combat maneuver countermeasure strategy, and guides our UCAV to conduct action selection. The algorithm reduces the correlation between samples through multi-threading asynchronous learning. Finally, the effectiveness and feasibility of the method are verified in three different air combat scenarios. Full article
(This article belongs to the Special Issue Advanced Control of Unmanned Aerial Vehicles (UAV))
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24 pages, 11594 KiB  
Article
A Trajectory Tracking Approach for Aerial Manipulators Using Nonsingular Global Fast Terminal Sliding Mode and an RBF Neural Network
by Lirui Shen, Pengjun Mao, Qian Fang and Jun Wang
Machines 2022, 10(11), 1021; https://doi.org/10.3390/machines10111021 - 3 Nov 2022
Cited by 5 | Viewed by 1823
Abstract
An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics [...] Read more.
An unmanned aerial manipulator (UAM) is a novel flying robot consisting of an unmanned aerial vehicle (UAV) and a multi-degree-of-freedom (DoF) robotic arm. It can actively interact with the environment to conduct dangerous or inaccessible tasks for humans. Owing to the underactuated characteristics of UAVs and the coupling generated by the rigid connection with the manipulator, robustness and a high-precision controller are critical for UAMs. In this paper, we propose a nonsingular global fast terminal sliding mode (NGFTSM) controller for UAMs to track the expected trajectory under the influence of disturbances based on a reasonably simplified UAM system dynamics model. To achieve active anti-disturbance and high tracking accuracy in a UAM system, we incorporate an RBF neural network into the controller to estimate lumped disturbances, including internal coupling and external disturbances. The controller and neural network are derived according to Lyapunov theory to ensure the system’s stability. In addition, we propose a set of illustrative metrics to evaluate the performance of the designed controller and compare it with other controllers by simulations. The results show that the proposed controller can effectively enhance the robustness and accuracy of a UAM system with satisfactory convergence. The experimental results also verify the effectiveness of the proposed controller. Full article
(This article belongs to the Special Issue Advanced Control of Unmanned Aerial Vehicles (UAV))
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