Guidance and Control Systems of Aerospace Vehicles

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

Deadline for manuscript submissions: 30 September 2026 | Viewed by 2845

Special Issue Editors

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

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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

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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

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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

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Published Papers (2 papers)

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21 pages, 3876 KB  
Article
A Fast Robust Integrated Guidance and Control Scheme for Flight Vehicles Based on Convergence Rate Estimation Mechanism
by Tianyu Ye, Wanying Xu, Yongbin Zheng, Qingwen Ma, Peisen Zhu and Yangyang Deng
Aerospace 2026, 13(1), 72; https://doi.org/10.3390/aerospace13010072 - 9 Jan 2026
Viewed by 294
Abstract
Convergence rate is a key performance index for flight vehicles, and accelerating it remains a critical open issue. In this paper, a fast robust integrated guidance and control scheme for flight vehicles based on convergence rate estimation mechanism is proposed, which improves the [...] Read more.
Convergence rate is a key performance index for flight vehicles, and accelerating it remains a critical open issue. In this paper, a fast robust integrated guidance and control scheme for flight vehicles based on convergence rate estimation mechanism is proposed, which improves the control performance and interception accuracy of flight vehicles. In the fast robust control scheme, a convergence rate indicator for integrated guidance and control systems is developed to measure the impact on convergence rate imposed by model nonlinearities and couplings within flight vehicles. Based on the indicator, the influences on convergence rate are transformed and injected into controllers to accelerate the convergence of flight vehicles. The unmatched lumped uncertainties in flight vehicle dynamics are addressed by a backstepping control method and finite-time convergence disturbance observers, which improves the robustness of the vehicle’s control system. Furthermore, the stability analysis of the closed-loop system is performed via the Lyapunov stability theorem. Extensive numerical simulations are conducted to verify the effectiveness and interception performance of the proposed method, and the comparison results confirm that it outperforms three other recently developed robust control methods. Full article
(This article belongs to the Special Issue Guidance and Control Systems of Aerospace Vehicles)
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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
Cited by 1 | Viewed by 2165
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)
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