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Applied Sciences
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Development and Application of Unmanned Aerial Vehicle Control Technology
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Development and Application of Unmanned Aerial Vehicle Control Technology

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Aerospace Science and Engineering".

Deadline for manuscript submissions: 20 July 2025 | Viewed by 6690

Special Issue Editors

Dr. Xiaoping Xu

E-Mail Website
Guest Editor
1. School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
2. National Key Laboratory of Aircraft Configuration Design, Xi’an 710072, China
Interests: advanced UAV aerodynamics; flight stability and control; autonomous flight
Dr. Rui Wang

E-Mail Website
Guest Editor
School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China
Interests: UAV flight dynamics; flight stability and control
Dr. Rosario Pecora

E-Mail Website
Guest Editor
Department of Industrial Engineering—Aerospace Division, University of Naples “Federico II”, Via Claudio, 21, 80125 Napoli, NA, Italy
Interests: smart structures; smart aircraft technologies; morphing structures; structural dynamics; vibration control; dynamic aeroelasticity; non-linear dynamics; mechanics and experimental dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs) have seen rapid development and widespread application across various fields, including military, agriculture, logistics, environmental monitoring, and disaster management. Their versatility and capability to perform tasks that are either dangerous, difficult, or impossible for humans have catalyzed their widespread adoption. The control technology behind UAVs is a critical area of research that ensures these systems can perform complex tasks autonomously, safely, and efficiently. The evolution of UAV control systems encompasses various aspects including advanced control algorithms, autonomous navigation, sensor integration, and real-time decision-making processes. With the rise of machine learning and artificial intelligence, UAVs are now capable of performing more complex tasks with higher degrees of autonomy. This Special Issue aims to collate pioneering research that explores the latest developments in UAV control technologies and their practical applications. Potential topics of interest include, but are not limited to, the following:

  • Advanced control algorithms for UAVs;
  • Autonomous navigation and path optimization;
  • Multi-sensor integration and data fusion;
  • Real-time autonomous decision-making systems;
  • Swarm intelligence and collaborative control;
  • Robust control in adverse environments;
  • Artificial intelligence and machine learning in UAV control;
  • Human–UAV interaction and intuitive control interfaces;
  • Safety, security, and privacy in UAV operations;
  • Practical applications and case studies of UAVs.

Dr. Xiaoping Xu
Dr. Rui Wang
Dr. Rosario Pecora
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. Applied Sciences is an international peer-reviewed open access semimonthly 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

  • advanced control algorithms
  • autonomous navigation
  • multi-sensor integration
  • swarm intelligence
  • artificial intelligence
  • human–UAV interaction
  • practical applications

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Published Papers (4 papers)

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Research

24 pages, 8983 KiB  
Article
Cost-Effective Autonomous Drone Navigation Using Reinforcement Learning: Simulation and Real-World Validation
by Tomasz Czarnecki, Marek Stawowy and Adam Kadłubowski
Appl. Sci. 2025, 15(1), 179; https://doi.org/10.3390/app15010179 - 28 Dec 2024
Viewed by 2639
Abstract
Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The [...] Read more.
Artificial intelligence (AI) is used in tasks that usually require human intelligence. The motivation behind this study is the growing interest in deploying AI in public spaces, particularly in autonomous vehicles such as flying drones, to address challenges in navigation and control. The primary challenge lies in developing a robust, cost-effective system capable of autonomous navigation in real-world environments, handling obstacles, and adapting to dynamic conditions. To tackle this, we propose a novel approach integrating machine learning (ML) algorithms, specifically, reinforcement learning (RL), with a comprehensive simulation and testing framework. Reinforcement learning machine algorithms designed to solve problems requiring optimization of the solution for the highest possible reward were used. It was assumed that the algorithms do not have to be created from scratch, but they need a well-defined training environment that will appropriately reward or punish the actions taken. This study aims to develop and implement a novel approach to autonomous drone navigation using machine learning (ML) algorithms. The primary innovation lies in the comprehensive integration of ML algorithms with a real-world drone control system, encompassing both simulations and real-world testing. A vital component of this approach is creating a multi-stage training environment that accurately replicates actual flight conditions and progressively increases the complexity of scenarios, ensuring a robust evaluation of algorithm performance. This research also introduces a new approach to optimizing system cost and accessibility. It involves using commercially available, cost-effective drones and open-source or free simulation tools, significantly reducing entry barriers for potential users. A critical aspect of this study is to assess whether affordable components can provide sufficient accuracy and stability without compromising system quality. The authors developed a system capable of autonomously determining optimal flight paths and controlling the drone, allowing it to avoid obstacles and respond to dynamic conditions in real time. The performance of the trained algorithms was confirmed through simulations and real-world flights, which allowed for assessing their usefulness in practical drone navigation scenarios. Full article
(This article belongs to the Special Issue Development and Application of Unmanned Aerial Vehicle Control Technology)
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35 pages, 20549 KiB  
Article
Research on the Dynamics Model and Jump/Drop Control Strategy of Distributed-Propeller Unmanned Aerial Vehicles
by Yansheng Geng, Xinxin Chen, Yinglong He and Xiaoping Xu
Appl. Sci. 2024, 14(24), 12040; https://doi.org/10.3390/app142412040 - 23 Dec 2024
Viewed by 818
Abstract
Compared with conventional drones, distributed powered drones have significant advantages in handling stability characteristics, lift and drag characteristics, and takeoff and landing performance. However, there are also challenges such as aerodynamic interference of multi powered slipstream, distributed-power/wing strong-coupling dynamic modeling, and redundant control [...] Read more.
Compared with conventional drones, distributed powered drones have significant advantages in handling stability characteristics, lift and drag characteristics, and takeoff and landing performance. However, there are also challenges such as aerodynamic interference of multi powered slipstream, distributed-power/wing strong-coupling dynamic modeling, and redundant control allocation of distributed-power control mechanisms. The paper has carried out the research on the dynamic modeling method, flight dynamics characteristics analysis, and the design of the control strategy of the jump and steep descent of the distributed dynamic configuration fixed wing unmanned aerial vehicle. A comprehensive aircraft dynamic model considering the influence of propeller slip on aerodynamics was established by combining theoretical derivation with flight experiment data correction. By comparing and analyzing the longitudinal and lateral heading control efficiency of unmanned aerial vehicles under rudder deflection and dynamic differential, a control concept of roll co-ordination control yaw combined with left and right dynamic differential is proposed. Digital simulation and flight tests showed that the established full aircraft dynamics model can accurately reflect the motion laws of distributed-power takeoff and landing unmanned aerial vehicles. The designed takeoff and landing strategy and control scheme can successfully achieve unmanned aerial vehicle takeoff and landing and perform cruising flight tasks. Full article
(This article belongs to the Special Issue Development and Application of Unmanned Aerial Vehicle Control Technology)
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13 pages, 1742 KiB  
Article
Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments
by Andrea Tonini, Mauro Castelli, Jordan Steven Bates, Nyi Nyi Nyan Lin and Marco Painho
Appl. Sci. 2024, 14(20), 9493; https://doi.org/10.3390/app14209493 - 17 Oct 2024
Cited by 2 | Viewed by 1317
Abstract
Estimating the location of unmanned aerial vehicles (UAVs) within a global coordinate system can be achieved by correlating known world points with their corresponding image projections captured by the vehicle’s camera. Reducing the number of required world points may lower the computational requirements [...] Read more.
Estimating the location of unmanned aerial vehicles (UAVs) within a global coordinate system can be achieved by correlating known world points with their corresponding image projections captured by the vehicle’s camera. Reducing the number of required world points may lower the computational requirements needed for such estimation. This paper introduces a novel method for determining the absolute position of aerial vehicles using only two known coordinate points that reduce the calculation complexity and, therefore, the computation time. The essential parameters for this calculation include the camera’s focal length, detector dimensions, and the Euler angles for Pitch and Roll. The Yaw angle is not required, which is beneficial because Yaw is more susceptible to inaccuracies due to environmental factors. The vehicle’s position is determined through a sequence of straightforward rigid transformations, eliminating the need for additional points or iterative processes for verification. The proposed method was tested using a Digital Elevation Model (DEM) created via LiDAR and 11 aerial images captured by a UAV. The results were compared against Global Navigation Satellite Systems (GNSSs) data and other common image pose estimation methodologies. While the available data did not permit precise error quantification, the method demonstrated performance comparable to GNSS-based approaches. Full article
(This article belongs to the Special Issue Development and Application of Unmanned Aerial Vehicle Control Technology)
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15 pages, 564 KiB  
Article
Online Trajectory Replanning for Avoiding Moving Obstacles Using Fusion Prediction and Gradient-Based Optimization
by Qianyi Fu, Wenjie Zhao, Shiyu Fang, Yiwen Zhu, Jun Li and Qili Chen
Appl. Sci. 2024, 14(18), 8339; https://doi.org/10.3390/app14188339 - 16 Sep 2024
Viewed by 1149
Abstract
In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the [...] Read more.
In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system’s safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation. Full article
(This article belongs to the Special Issue Development and Application of Unmanned Aerial Vehicle Control Technology)
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