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Machines
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Intelligent Control Techniques for Unmanned Aerial Vehicles
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Intelligent Control Techniques for Unmanned Aerial Vehicles

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 July 2026 | Viewed by 3942

Special Issue Editors

Dr. Maolong Lv

E-Mail Website
Guest Editor
Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an 710051, China
Interests: reinforcement learning-based decision-making; UAV swarms; intelligent air combat; man-unmanned cooperative control
Special Issues, Collections and Topics in MDPI journals
Dr. Junkai Ren

E-Mail Website
Guest Editor
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
Interests: reinforcement learning; autonomous robots; unmanned aerial vehicles; UGV
Prof. Dr. Haibin Duan

E-Mail Website
Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: unmanned aerial vehicles swarms; autonomous navigation; autonomous control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have rapidly evolved from niche tools to essential assets across a broad range of industries. UAVs are now integral to fields such as agriculture, surveillance, search and rescue, logistics, and environmental monitoring. A key factor driving their effectiveness is the adoption of intelligent control techniques, which enable UAVs to operate autonomously, adapt to dynamic environments, and perform complex tasks with high precision. As UAVs are often deployed in unpredictable or hazardous environments, traditional control methods are frequently insufficient to ensure reliability and safety. This is where intelligent control technologies, such as adaptive control, fuzzy logic, reinforcement learning, and neural networks, come into play. These methods allow for the UAVs to learn from their surroundings, make real-time decisions, and adjust their behaviors without human intervention, leading to improved mission success rates and operational efficiency.

The objective of this Special Issue is to explore the latest advancements in intelligent control techniques for UAVs, with an emphasis on algorithms, architectures, and applications that enhance the autonomy, reliability, and robustness of UAV systems. This Special Issue invites original research articles that contribute significantly to theoretical, numerical, and experimental developments in UAV control, as well as application-driven innovations. Review articles highlighting the state-of-the-art in UAV intelligent control are also encouraged.

Potential topics include, but are not limited to, the following:

  • Adaptive control techniques for UAVs;
  • Fuzzy logic and hybrid control systems;
  • Reinforcement learning and deep learning for autonomous UAVs;
  • Path planning and trajectory optimization;
  • Real-time decision-making algorithms;
  • Robust control for uncertain and dynamic environments;
  • UAV swarm intelligence and multi-agent systems;
  • Fault detection and diagnosis in UAV systems;
  • Sensor fusion and perception for autonomous flight;
  • Vision-based control for obstacle avoidance and navigation;
  • Safety and reliability analysis in UAV systems;
  • Autonomous UAVs for precision agriculture, disaster response, and surveillance;
  • Human–UAV interactions and collaborative systems;
  • 5G and IoT integration for UAV control systems;
  • UAV control in GPS-denied environments;
  • Advanced simulation models for UAV control and testing.

Dr. Maolong Lv
Dr. Junkai Ren
Prof. Dr. Haibin Duan
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 250 words) can be sent to the Editorial Office for assessment.

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

  • unmanned aerial vehicles
  • intelligent control
  • stability analysis
  • adaptive learning control

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Published Papers (4 papers)

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Research

24 pages, 5102 KB  
Article
Towards Robust Physical Adversarial Attacks on UAV Object Detection: A Multi-Dimensional Feature Optimization Approach
by Hailong Xi, Le Ru, Jiwei Tian, Wenfei Wang, Rui Zhu, Shiliang Li, Zhenghao Zhang, Longhao Liu and Xiaohui Luan
Machines 2025, 13(11), 1060; https://doi.org/10.3390/machines13111060 - 17 Nov 2025
Viewed by 530
Abstract
Deep neural network (DNN)-based object detection has been extensively implemented in Unmanned Aerial Vehicles (UAVs). However, these architectures reveal significant vulnerabilities when faced with adversarial attacks, particularly the physically realizable adversarial patches, which are highly practicable. Existing methods for generating adversarial patches are [...] Read more.
Deep neural network (DNN)-based object detection has been extensively implemented in Unmanned Aerial Vehicles (UAVs). However, these architectures reveal significant vulnerabilities when faced with adversarial attacks, particularly the physically realizable adversarial patches, which are highly practicable. Existing methods for generating adversarial patches are easily affected by factors such as motion blur and color distortion, leading to a decline in the attack success rate (ASR). To address these limitations, a low-frequency robust adversarial patch (LFRAP) generation framework that integrates three dimensions of color, texture, and frequency domain is proposed. Firstly, a dynamic extraction mechanism for the environmental color pool based on clustering is proposed. This mechanism not only improves the degree of environmental integration but also reduces printing losses. Secondly, mathematical modeling of the effects of Unmanned Aerial Vehicle (UAV) high-speed motion is incorporated into the patch training process. The specialized texture derived from this modeling alleviates patch blurring and the subsequent decrease in attack efficiency caused by the high-speed movement of UAVs. Finally, a frequency domain separation strategy is introduced in the generation process to optimize the frequency space distribution, thereby reducing information loss during image recapture by UAV vision systems. The experimental results show that this framework increased the environment integration rate of the generated patches by 18.9%, and the attack success rate under the condition of motion blur increased by 19.2%, which significantly outperformed conventional methods. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
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27 pages, 28182 KB  
Article
Addressing Local Minima in Path Planning for Drones with Reinforcement Learning-Based Vortex Artificial Potential Fields
by Boyi Xiao, Lujun Wan, Xueyan Han, Zhilong Xi, Chenbo Ding and Qiang Li
Machines 2025, 13(7), 600; https://doi.org/10.3390/machines13070600 - 11 Jul 2025
Viewed by 898
Abstract
In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper [...] Read more.
In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
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26 pages, 6752 KB  
Article
A Q-Learning Crested Porcupine Optimizer for Adaptive UAV Path Planning
by Jiandong Liu, Yuejun He, Bing Shen, Jing Wang, Penggang Wang, Guoqing Zhang, Xiang Zhuang, Ran Chen and Wei Luo
Machines 2025, 13(7), 566; https://doi.org/10.3390/machines13070566 - 30 Jun 2025
Cited by 2 | Viewed by 870
Abstract
Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an [...] Read more.
Unmanned Aerial Vehicle (UAV) path planning is critical for ensuring flight safety and enhancing mission execution efficiency. This problem is typically formulated as a complex, multi-constrained, and nonlinear optimization task, often addressed using meta-heuristic algorithms. The Crested Porcupine Optimizer (CPO) has become an excellent method to solve this problem; however, the standard CPO has limitations, such as the lack of adaptive parameter tuning to adapt to complex environments, slow convergence, and the tendency to fall into local optimal solutions. To address these issues, this paper proposes an algorithm named QCPO, which integrates CPO with Q-learning to improve UAV path optimization performance. Q-learning is employed to adaptively adjust the key parameters of the CPO, thereby overcoming the limitations of traditional fixed-parameter settings. Inspired by the porcupine’s defense mechanisms, a novel audiovisual coordination strategy is introduced to balance visual and auditory responses, accelerating convergence in the early optimization stages. A refined position update mechanism is designed to prevent excessive step sizes and boundary violations, enhancing the algorithm’s global search capability. A B-spline-based trajectory smoothing method is also incorporated to improve the feasibility and smoothness of the planned paths. In this paper, we compare QCPO with four outstanding heuristics, and QCPO achieves the lowest path cost in all three test scenarios, with path cost reductions of 30.23%, 26.41%, and 33.47%, respectively, compared to standard CPO. The experimental results confirm that QCPO offers an efficient and safe solution for UAV path planning. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
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23 pages, 1930 KB  
Article
Event-Driven Prescribed-Time Tracking Control for Multiple UAVs with Flight State Constraints
by Xueyan Han, Peng Yu, Maolong Lv, Yuyuan Shi and Ning Wang
Machines 2025, 13(3), 192; https://doi.org/10.3390/machines13030192 - 27 Feb 2025
Cited by 1 | Viewed by 673
Abstract
Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose [...] Read more.
Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose a novel prescribed-time command filtered backstepping approach to effectively tackle the issues of complexity explosion and singularities. By utilizing a state-transition function, we manage asymmetric time-varying state constraints, including limitations on speed, roll, yaw, and pitch angles in UAVs. The theoretical analysis demonstrates that all signals in the 6-DOF UAV system remain bounded, with tracking errors converging to the origin within the prescribed time. Finally, simulation results validate the effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
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