Multi-Spacecraft Coordination and Intelligent Aircraft Autonomous Control

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 753

Special Issue Editor


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Guest Editor
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Interests: automation control; coordination control; path planning

Special Issue Information

Dear Colleagues,

With the rapid advancement of aerospace technology, the demand for complex space missions (e.g., on-orbit servicing, deep-space exploration, and satellite constellation operations) and high-performance aircraft systems (e.g., unmanned aerial vehicles, civil airliners, and future urban air mobility) has increasingly highlighted two core challenges: multi-agent coordination under extreme environments and autonomous decision-making with dynamic uncertainties. Against this backdrop, this Special Issue, titled "Multi-Spacecraft Coordination and Intelligent Aircraft Autonomous Control", will compile cutting-edge research, innovative methodologies, and practical applications that address fundamental scientific and engineering challenges in these two interconnected fields. It will establish a high-level academic platform for researchers, engineers, and practitioners to share insights and foster technological breakthroughs. The research scope includes, but is not limited to, the following:

  1. Multi-Spacecraft Coordination Control;
  2. Unmanned Systems Coordination Control;
  3. Multi-Aircraft Path Planning;
  4. Aircraft/Spacecraft Discrete Control;
  5. Aircraft/Spacecraft Intelligent Control;
  6. Aircraft/Spacecraft Fractional-order Control.

Dr. Shuyi Shao
Guest Editor

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Keywords

  • intelligent control
  • flight control
  • path planning
  • discrete control
  • fractional-order control

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Published Papers (1 paper)

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Research

32 pages, 4167 KB  
Article
Dynamic Time-Window Nash Equilibrium Strategies for Spacecraft Pursuit–Evasion Games Under Incomplete Strategies
by Lei Sun, Zengliang Han, Yuhui Wang, Binpeng Tian and Panxing Huang
Machines 2026, 14(3), 280; https://doi.org/10.3390/machines14030280 - 2 Mar 2026
Viewed by 400
Abstract
Spacecraft pursuit–evasion in contested environments is complicated by strategic incompleteness: the evader can switch maneuvering modes and deploy multi-domain countermeasures that degrade the pursuer’s perception, leading to non-stationary information and distributionally ambiguous interference statistics. A dynamic time-window Nash equilibrium framework is developed for [...] Read more.
Spacecraft pursuit–evasion in contested environments is complicated by strategic incompleteness: the evader can switch maneuvering modes and deploy multi-domain countermeasures that degrade the pursuer’s perception, leading to non-stationary information and distributionally ambiguous interference statistics. A dynamic time-window Nash equilibrium framework is developed for linearized Local Vertical Local Horizontal (LVLH) relative motion under interference-induced uncertainty. Perceptual degradation is modeled via an evidence–theoretic belief representation, and the Jensen–Shannon (JS) divergence is introduced to quantify discrepancies between nominal and interference-corrupted beliefs. The divergence metric drives an adaptive time-window partitioning policy and an uncertainty-aware running cost that balances nominal performance objectives with robustness regularization during high-degradation intervals. In each time window, sufficient conditions are provided for the existence of a local Nash equilibrium, and equilibrium strategies are characterized by the Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation. A global consistency result is established: assuming state continuity, additive cost decomposition, and dynamic-programming compatibility at window boundaries, concatenating the window-wise equilibria yields a Nash equilibrium over the entire horizon. Unlike conventional receding-horizon differential games with a fixed replanning grid, the proposed policy partitions the horizon online in response to perceptual-degradation events and stitches adjacent windows through a continuation value. This boundary stitching enables the global consistency guarantee under additive costs and state continuity. To hedge against ambiguity in interference intensity, a variational distributionally robust optimization (DRO) problem with moment-constrained ambiguity sets is formulated, and the dual worst-case distribution is derived. The resulting Karush–Kuhn–Tucker (KKT) system is reformulated as a finite-dimensional variational inequality, for which an accelerated Alternating Direction Method of Multipliers (ADMM) operator-splitting solver is proposed for efficient real-time computation. Numerical simulations validate the framework and demonstrate improved robustness and computational scalability under time-varying interference compared with fixed-window baselines. Full article
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