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Aerospace
Special Issues
New Perspective on Flight Guidance, Control and Dynamics
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New Perspective on Flight Guidance, Control and Dynamics

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 10198

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 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

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

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

Published Papers (7 papers)

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Research

23 pages, 5316 KB  
Article
Prescribed Performance-Based Predefined-Time Sliding Mode Control for Hypersonic Vehicles
by Zihao Cheng, Guangbin Cai, Yiming Shang, Xin Li, Ziqi Ye and Yonghua Fan
Aerospace 2026, 13(5), 453; https://doi.org/10.3390/aerospace13050453 - 10 May 2026
Viewed by 239
Abstract
This paper presents a systematic design of a predefined-time sliding mode controller with integrated prescribed performance strategies, tailored to the longitudinal dynamics of hypersonic vehicles (HSVs) operating under strict real-time and high-precision requirements. A prescribed error transformation function is introduced to simultaneously constrain [...] Read more.
This paper presents a systematic design of a predefined-time sliding mode controller with integrated prescribed performance strategies, tailored to the longitudinal dynamics of hypersonic vehicles (HSVs) operating under strict real-time and high-precision requirements. A prescribed error transformation function is introduced to simultaneously constrain both transient and steady-state behaviors. This transformation converts the original constrained tracking problem into an equivalent unconstrained stabilization problem, thereby simplifying the controller synthesis. Based on the decomposed control-oriented state-space equations, separate sliding mode controllers are designed for the velocity and attitude subsystems. The proposed strategy guarantees that the tracking errors converge to zero within a user-predefined time, while strictly satisfying the prescribed performance bounds at every stage of the closed-loop response. The efficacy of the method is validated through numerical simulations. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
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21 pages, 2720 KB  
Article
Adaptive Neural Barrier Function-Based Fast Terminal Sliding Mode Control for Bionic Aerial Manipulators in Canopy Sampling
by Xiaohu Chen, Li Ding, Wenfeng Wu and Hongtao Wu
Aerospace 2026, 13(4), 392; https://doi.org/10.3390/aerospace13040392 - 21 Apr 2026
Viewed by 324
Abstract
This paper proposes a novel adaptive sliding mode control strategy for bionic aerial manipulators performing canopy-sampling tasks. Specifically, an adaptive neural barrier function-based fast terminal sliding mode control (BFASMC-NN) scheme is developed to address the joint-space trajectory tracking problem by integrating fast continuous [...] Read more.
This paper proposes a novel adaptive sliding mode control strategy for bionic aerial manipulators performing canopy-sampling tasks. Specifically, an adaptive neural barrier function-based fast terminal sliding mode control (BFASMC-NN) scheme is developed to address the joint-space trajectory tracking problem by integrating fast continuous nonsingular terminal sliding mode control (FNTSMC), neural networks (NNs), and barrier functions (BFs). The aerial manipulator is modeled as a rootless system, and its kinematic and dynamic characteristics are analyzed separately. Radial basis function neural networks (RBF-NNs) are introduced to approximate lumped disturbances, while BFs are incorporated to mitigate the effects of joint input saturation. Meanwhile, FNTSMC is employed to guarantee finite-time convergence of the system states. The stability of the closed-loop system is rigorously proven based on Lyapunov stability theory. Two simulation studies are conducted to validate the proposed method, and the results demonstrate that it achieves stronger disturbance rejection capability, faster convergence, and higher tracking accuracy than existing approaches. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
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19 pages, 1271 KB  
Article
Efficient Reachable Domain Search-Tracking for Cislunar Non-Cooperative Targets via Designed Quadrature
by Kaige Li, Yidi Wang and Wei Zheng
Aerospace 2025, 12(12), 1056; https://doi.org/10.3390/aerospace12121056 - 27 Nov 2025
Viewed by 1221
Abstract
To address the triple challenges of data sparsity, highly nonlinear dynamics, and maneuver uncertainty in tracking non-cooperative targets in cislunar space, we propose a collaborative framework combining Particle Filter (PF) and Unscented Kalman Filter (UKF). This framework optimizes search efficiency through a two-phase [...] Read more.
To address the triple challenges of data sparsity, highly nonlinear dynamics, and maneuver uncertainty in tracking non-cooperative targets in cislunar space, we propose a collaborative framework combining Particle Filter (PF) and Unscented Kalman Filter (UKF). This framework optimizes search efficiency through a two-phase strategy: in the search phase, PF constructs the target reachable domain and leverages undetected information to dynamically shrink the search scope; upon target detection, the framework switches to UKF for high-precision and low-overhead tracking. To overcome the computational bottleneck in high-dimensional reachable domain integration, we integrate a non-product-type Designed Quadrature (DQ) method—one that generates minimal quadrature point sets to replace traditional Monte Carlo sampling by matching the moment conditions of mixed distributions via Gauss–Newton optimization. Distinct from existing single-filter or reachability modeling approaches, the key novelties of this work lie in a two-phase PF-UKF switching framework tailored to the unique cislunar environment resolving the trade-off between search capability and computational efficiency and integration of the non-product DQ method to break the dimensionality curse in high-dimensional reachable domain computation ensuring both moment-matching accuracy and real-time performance. This work holds potential to support space domain awareness and cislunar mission safety: reliable tracking of non-cooperative targets is a key prerequisite for avoiding collisions, safeguarding space assets, and enabling effective space defense, and the proposed framework provides a feasible technical path for this goal through simulation validation. Simulations demonstrate that on a three-dimensional Distant Retrograde Orbit (DRO) observation platform, successful recapture of cislunar transfer orbit targets can be achieved. Under fifth-order accuracy conditions, the system exhibits a position error of 3.745×10−1km and a velocity tracking error of 9.703×10−3m/s for target search-and-tracking tasks, with a system response time of 1.8343 h. Compared with the traditional PF + numerical integration method, our proposed PF-UKF framework achieves an 86.7% reduction in time cost and a 24.1% reduction in position error. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
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24 pages, 1678 KB  
Article
A Decoupled Sliding Mode Predictive Control of a Hypersonic Vehicle Based on an Extreme Learning Machine
by Zhihua Lin, Haiyan Gao, Jianbin Zeng and Weiqiang Tang
Aerospace 2025, 12(11), 981; https://doi.org/10.3390/aerospace12110981 - 31 Oct 2025
Viewed by 846
Abstract
A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem [...] Read more.
A sliding mode predictive control (SMPC) scheme integrated with an extreme learning machine (ELM) disturbance observer is proposed for the trajectory tracking of a flexible air-breathing hypersonic vehicle (FAHV). To streamline the controller design, the longitudinal model is decoupled into a velocity subsystem and an altitude subsystem. For the velocity subsystem, a proportional-integral sliding mode surface is designed, and the control law is derived by minimizing a cost function that weights the predicted sliding mode surface and the control input. For the altitude subsystem, a backstepping control framework is adopted, with the SMPC strategy embedded in each step. Multi-source disturbances are modeled as composite additive disturbances, and an ELM-based neural network observer is constructed for their real-time estimation and compensation, thereby enhancing system robustness. The semi-globally uniformly ultimately bounded (SGUUB) stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Simulation results demonstrate the comprehensive superiority of the proposed method: it achieves reductions in Root Mean Square Error (RMSE) of 99.60% and 99.22% for velocity and altitude tracking, respectively, compared to Prescribed Performance Control with Backstepping Control (PPCBSC), and reductions of 98.48% and 97.12% relative to Terminal Sliding Mode Control (TSMC). Under parameter uncertainties, the developed ELM observer outperforms RBF-based observer and Extended State Observer (ESO) by significantly reducing tracking errors. These findings validate the high precision and strong robustness of the proposed approach. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
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21 pages, 4380 KB  
Article
Midcourse Guidance via Variable-Discrete-Scale Sequential Convex Programming
by Jinlin Zhang, Jiong Li, Lei Shao, Jikun Ye and Yangchao He
Aerospace 2025, 12(11), 952; https://doi.org/10.3390/aerospace12110952 - 24 Oct 2025
Viewed by 806
Abstract
To address the challenges of strong nonlinearity, stringent terminal constraints, and the trade-off between computational efficiency and accuracy in the midcourse guidance trajectory optimization problem of aerodynamically controlled interceptors, this paper proposes a variable-discrete-scale sequential convex programming (SCP) method. Firstly, a dynamic model [...] Read more.
To address the challenges of strong nonlinearity, stringent terminal constraints, and the trade-off between computational efficiency and accuracy in the midcourse guidance trajectory optimization problem of aerodynamically controlled interceptors, this paper proposes a variable-discrete-scale sequential convex programming (SCP) method. Firstly, a dynamic model is established by introducing the range domain to replace the traditional time domain, thereby reducing the approximation error of the planned trajectory. Second, to overcome the critical issues of solution space restriction and trajectory divergence caused by terminal equality constraints, a terminal error-proportional relaxation approach is proposed. Subsequently, an improved second-order cone programming (SOCP) formulation is developed through systematic integration of three key techniques: terminal error-proportional relaxation, variable trust region, and path normalization. Finally, an initial trajectory generation algorithm is proposed, upon which a variable-discrete-scale optimization framework is constructed. This framework incorporates a residual-driven discrete-scale adaptation mechanism, which balances discretization errors and computational load. Numerical simulation results indicate that under large discretization scales, the computation time required by the improved SOCP is only about 5.4% of that of GPOPS-II. For small-discretization-scale optimization, the SCP method with the variable discretization framework demonstrates high efficiency, achieving comparable accuracy to GPOPS-II while reducing the computation time to approximately 7.4% of that required by GPOPS-II. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
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19 pages, 3612 KB  
Article
Phase-Adaptive Reinforcement Learning for Self-Tuning PID Control of Cruise Missiles
by Chang Tan, Jianfeng Wang, Hong Cai, Sen Hu, Bangchu Zhang and Weiyu Zhu
Aerospace 2025, 12(9), 849; https://doi.org/10.3390/aerospace12090849 - 20 Sep 2025
Viewed by 1509
Abstract
Conventional fixed-gain PID controllers face inherent limitations in maintaining optimal performance across the diverse and dynamic flight phases of cruise missiles. To overcome these challenges, we propose Time-Fusion Proximal Policy Optimization (TF-PPO), a novel adaptive reinforcement learning framework designed specifically for cruise missile [...] Read more.
Conventional fixed-gain PID controllers face inherent limitations in maintaining optimal performance across the diverse and dynamic flight phases of cruise missiles. To overcome these challenges, we propose Time-Fusion Proximal Policy Optimization (TF-PPO), a novel adaptive reinforcement learning framework designed specifically for cruise missile control. TF-PPO synergistically integrates Long Short-Term Memory (LSTM) networks for enhanced temporal state perception and phase-specific reward engineering enabling self-evolution of PID parameters. Extensive hardware-in-the-loop experiments tailored to cruise missile dynamics demonstrate that TF-PPO achieves a 36.3% improvement in control accuracy over conventional PID methods. The proposed framework provides a robust, high-precision adaptive control solution capable of enhancing the performance of cruise missile systems under varying operational. Full article
(This article belongs to the Special Issue New Perspective on Flight Guidance, Control and Dynamics)
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24 pages, 5286 KB  
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
Cited by 1 | Viewed by 3913
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)
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