Guidance and Control Systems of Aerospace Vehicles

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1738

Special Issue Editors

School of Astronautics, Northwestern Polytechnical University, Xi’an, China
Interests: guidance, control and intelligent algorithms on aerospace vehicles

E-Mail Website
Guest Editor
School of Aerospace Science and Technology, Xidian University, Xi’an, China
Interests: fault detection and fault- tolerant control; advanced control theory and its application in aerospace systems

E-Mail
Guest Editor
School of Automation and Information Engineering, Xi'an University of Technology, Xi’an, China
Interests: trajectory planning; guidance; control of aerospace vehicles

Special Issue Information

Dear Colleagues,

Aerospace vehicles (including combined cycle propulsion vehicles, cross-domain morphing vehicles, hypersonic vehicles, and reusable launch vehicles) belong to a new generation of round-trip transportation systems capable of freely traveling through dense atmospheres, near-space, and orbital space. They break through the limitations of traditional aircraft and spacecraft, offering advantages such as low cost, convenience, and maneuverability. The requirements for multi-task, multi-mode operation, and high-speed maneuverability over a wide range of aerospace vehicles present numerous critical challenges for guidance and control technology, including precise modeling under the influence of multiple physical fields, rapid trajectory planning in complex cross-domain flight environments or under changing flight mission conditions, stability issues for guidance and control systems under strong disturbances and quickly variable parameters, and fault-tolerant control issues in the event of combined propulsion engine faults during flight and so on. Traditional methods generally rely on decoupling dynamics and small disturbance linearization, which are difficult to cope with the extreme nonlinearity, multi-physical field coupling, and strict performance requirements imposed by cross-domain flight environments. Therefore, there are still many fundamental research issues that need to be addressed in the design of guidance and control systems for aerospace vehicles, which will greatly facilitate future space exploration. To promote the development of space transportation technology, highlight the latest research findings, and provide a comprehensive overview of cutting-edge trends in guidance and control system design and its applications. This Special Issue aims to collect the latest advancements in guidance and control system design for aerospace vehicles, as well as share the latest research achievements related to intelligent and progressive guidance and control theories and experimental studies associated with aerospace vehicles. It primarily invites articles from the technical field, including but not limited to novel dynamic modeling analysis, trajectory planning, guidance and control method design, and experimental validation.

Dr. Zongyi Guo
Dr. Jing Chang
Dr. Guanjie Hu
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. 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

  • aerospace vehicles
  • reusable launch vehicles
  • combined cycle propulsion
  • advanced guidance and control
  • trajectory prediction/planning
  • morphing decision
  • prescribed performance control
  • intelligent guidance and control
  • AI algorithm and applications in aerospace vehicles

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
Select all
Export citation of selected articles as:

Research

28 pages, 44537 KB  
Article
Multi-UAV Cooperative Pursuit Planning via Communication-Aware Multi-Agent Reinforcement Learning
by Haojie Ren, Chunlei Han, Hao Pan, Jianjun Sun, Shuanglin Li, Dou An and Kunhao Hu
Aerospace 2025, 12(11), 993; https://doi.org/10.3390/aerospace12110993 - 6 Nov 2025
Viewed by 1407
Abstract
Cooperative pursuit using multi-UAV systems presents significant challenges in dynamic task allocation, real-time coordination, and trajectory optimization within complex environments. To address these issues, this paper proposes a reinforcement learning-based task planning framework that employs a distributed Actor–Critic architecture enhanced with bidirectional recurrent [...] Read more.
Cooperative pursuit using multi-UAV systems presents significant challenges in dynamic task allocation, real-time coordination, and trajectory optimization within complex environments. To address these issues, this paper proposes a reinforcement learning-based task planning framework that employs a distributed Actor–Critic architecture enhanced with bidirectional recurrent neural networks (BRNN). The pursuit–evasion scenario is modeled as a multi-agent Markov decision process, enabling each UAV to make informed decisions based on shared observations and coordinated strategies. A multi-stage reward function and a BRNN-driven communication mechanism are introduced to improve inter-agent collaboration and learning stability. Extensive simulations across various deployment scenarios, including 3-vs-1 and 5-vs-2 configurations, demonstrate that the proposed method achieves a success rate of at least 90% and reduces the average capture time by at least 19% compared to rule-based baselines, confirming its superior effectiveness, robustness, and scalability in cooperative pursuit missions. Full article
(This article belongs to the Special Issue Guidance and Control Systems of Aerospace Vehicles)
Show Figures

Figure 1

Back to TopTop