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 December 2025 | Viewed by 1337
Special Issue Editors
Interests: reinforcement learning-based decision-making; UAV swarms; intelligent air combat; man-unmanned cooperative control
Special Issues, Collections and Topics in MDPI journals
Interests: reinforcement learning; autonomous robots; unmanned aerial vehicles; UGV
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 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
- unmanned aerial vehicles
- intelligent control
- stability analysis
- adaptive learning control
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