Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.0
2.2
Journals
Aerospace
Special Issues
New Perspective on Flight Guidance, Control and Dynamics
share announcement

New Perspective on Flight Guidance, Control and Dynamics

A special issue of Aerospace (ISSN 2226-4310).

Deadline for manuscript submissions: 30 November 2025 | Viewed by 576

Special Issue Editors

Prof. Dr. Yuanli Cai

E-Mail Website
Guest Editor
Faculty of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, China
Interests: flight guidance; control and dynamics; modelling and simulation for complex systems; intelligent information processing and systems, etc.
Prof. Dr. Bangcheng Ai

E-Mail Website
Guest Editor
China Academy of Aerospace Aerodynamics, Beijing, China
Interests: aerodynamics; aerothermodynamics; flight mechanics, etc.

Special Issue Information

Dear Colleagues,

Flight guidance, control, and dynamics are important and fundamental issues in aerospace engineering. With the development of modern control theory and artificial intelligence technology, related research is entering a new stage, and many inspiring results have emerged.

This Special Issue is designed to capture current advances and prospects in the field of flight guidance, control, and dynamics. The relevant achievements can provide technical support for the modeling, planning, design, decision-making, and evaluation of a wide range of aerospace systems.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Flight Motion Modeling and Simulation.
  • Trajectory Optimization and Online Planning.
  • Cooperative Guidance and Formation Control.
  • Intelligent Offensive and Defensive Confrontation.
  • Multi-source Information Fusion and Intelligent Perception.
  • Aerodynamic Parameter Estimation and System.
  • Advanced Control System Design Methods and Techniques.
  • Flight-based Transportation.
  • Intelligent Design Theories and Technologies.

Prof. Dr. Yuanli Cai
Prof. Dr. Bangcheng Ai
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. Aerospace 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

  • flight modeling and simulation
  • flight guidance and control
  • artificial intelligence and optimization
  • information fusion and processing

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 5286 KiB  
Article
Graph Neural Network-Enhanced Multi-Agent Reinforcement Learning for Intelligent UAV Confrontation
by Kunhao Hu, Hao Pan, Chunlei Han, Jianjun Sun, Dou An and Shuanglin Li
Aerospace 2025, 12(8), 687; https://doi.org/10.3390/aerospace12080687 - 31 Jul 2025
Viewed by 312
Abstract
Unmanned aerial vehicles (UAVs) are widely used in surveillance and combat for their efficiency and autonomy, whilst complex, dynamic environments challenge the modeling of inter-agent relations and information transmission. This research proposes a novel UAV tactical choice-making algorithm utilizing graph neural networks to [...] Read more.
Unmanned aerial vehicles (UAVs) are widely used in surveillance and combat for their efficiency and autonomy, whilst complex, dynamic environments challenge the modeling of inter-agent relations and information transmission. This research proposes a novel UAV tactical choice-making algorithm utilizing graph neural networks to tackle these challenges. The proposed algorithm employs a graph neural network to process the observed state information, the convolved output of which is then fed into a reconstructed critic network incorporating a Laplacian convolution kernel. This research first enhances the accuracy of obtaining unstable state information in hostile environments. The proposed algorithm uses this information to train a more precise critic network. In turn, this improved critic network guides the actor network to make decisions that better meet the needs of the battlefield. Coupled with a policy transfer mechanism, this architecture significantly enhances the decision-making efficiency and environmental adaptability within the multi-agent system. Results from the experiments show that the average effectiveness of the proposed algorithm across the six planned scenarios is 97.4%, surpassing the baseline by 23.4%. In addition, the integration of transfer learning makes the network convergence speed three times faster than that of the baseline algorithm. This algorithm effectively improves the information transmission efficiency between the environment and the UAV and provides strong support for UAV formation combat. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop