Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition)

Special Issue Editors


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Guest Editor
School of Automation Science and Engineering, Beihang University, Beijing 100191, China
Interests: UAV; cooperative control; distributed optimization; game theory and NE seeking; security and resilience

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Guest Editor
School of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: reach-avoid differential games; neuro-symbolic stochastic games; multi-agent reinforcement learning
School of Cyber Science and Technology, Beihang University, Beijing 100191, China
Interests: resilient control of swarm system; security for cyber–physical systems; fault-tolerant control

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Guest Editor
Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: cooperative control; intelligent decision-making; task allocation and trajectory planning

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Guest Editor
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
Interests: cooperative control of multiagent systems; multiagent reinforcement learning

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue of Drones on “Distributed Control, Optimization, and Game of UAV Swarm Systems (2nd Edition)”.

UAV swarm systems can also be named multi-UAV systems consisting of multiple UAVs with neighboring interactions and have broad potential applications in various areas, such as intelligent transportation, disaster rescue, and cooperative detection. Distributed control, optimization, and game of UAV swarm systems have been a hot research topic in many scientific communities, especially the control and robotics communities. The main challenge is designing the controller or protocol using only neighboring relative information. Distributed control, optimization, and game of UAV swarm systems are promising because the emerging behavior features low cost, high scalability and flexibility, great robustness, and easy maintenance. Motivated by the facts stated above, more and more researchers are devoting themselves to obtaining sound results on this topic.

This Special Issue aims to collect papers (original research articles and review papers) to give insights about distributed control, optimization, and game of UAV swarm systems.

This Special Issue will welcome manuscripts that link the following themes:

  • Distributed control;
  • Formation control;
  • Distributed optimization;
  • Intelligent motion planning;
  • Game of UAV swarm systems;
  • Distributed Nash equilibrium seeking.

We look forward to receiving your original research articles and reviews.

Dr. Zhi Feng
Dr. Rui Yan
Dr. Yishi Liu
Dr. Xiaoduo Li
Dr. Qing Wang
Guest Editors

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Keywords

  • UAV swarm systems
  • distributed control
  • formation control
  • distributed optimization
  • intelligent motion planning
  • swarm game
  • distributed Nash equilibrium seeking

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Published Papers (1 paper)

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Research

17 pages, 6404 KiB  
Article
A Cooperative Decision-Making and Control Algorithm for UAV Formation Based on Non-Cooperative Game Theory
by Yongkang Jiao, Wenxing Fu, Xinying Cao, Kunhu Kou, Ji Tang, Rusong Shen, Yiyang Zhang and Haibo Du
Drones 2024, 8(12), 698; https://doi.org/10.3390/drones8120698 - 21 Nov 2024
Viewed by 492
Abstract
The formation control problem of distributed fixed-wing Unmanned Aerial Vehicles (UAVs) is investigated in this paper. By utilizing the theoretical foundations of non-cooperative game theory, a novel control strategy is introduced, which allows UAVs to autonomously determine the optimal flight trajectory without relying [...] Read more.
The formation control problem of distributed fixed-wing Unmanned Aerial Vehicles (UAVs) is investigated in this paper. By utilizing the theoretical foundations of non-cooperative game theory, a novel control strategy is introduced, which allows UAVs to autonomously determine the optimal flight trajectory without relying on centralized coordination while concurrently mitigating conflicts with other UAVs. By transforming the UAV model into a double integrator form, the control complexity is reduced. Additionally, the incorporation of a homogeneous differential disturbance observer enhances the UAV’s resilience against disturbances during the control process. Through the development and validation of a Nash equilibrium-based algorithm, it is demonstrated that UAVs can sustain a predefined formation flight and autonomously adapt their trajectories in complex environments. Simulations are presented to confirm the efficiency of the proposed method. Full article
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