Astrodynamics最新文献

The increase in human space activities has increased space threats, such as debris and failed spacecraft, which has made space orbital game technology a critical tool for ensuring space security. Although differential game theory remains the mainstream approach for classic space orbital games, its application faces growing limitations because of nonlinear orbital dynamics, model complexity, computational intractability, and poor real-time performance in increasingly complex scenarios. To address these challenges, numerous extended orbital game models have been developed in recent years, along with new solution methods. This review provides a systematic analysis and summary of model extensions and solution methods in orbital games. First, the extension directions of space orbital games are analyzed, and five extended game models based on the basic orbital game model are summarized: the closed-loop guidance model, incomplete information model, diverse scenario game model, impulsive thrust model, and multispacecraft game model. Afterward, research achievements in solution methods, including qualitative differential games, quantitative differential games, and artificial intelligence methods, are categorized and reviewed. Finally, current challenges and potential future research directions are discussed.

This paper presents an improved two-phase sequential convex programming (SCP) algorithm for reentry trajectory optimization. The continuous-time optimal control problem is first discretized using the Chebyshev pseudospectral method (CPM). A conformal mapping is introduced to redistribute the Chebyshev–Gauss–Lobatto (CGL) nodes, which effectively mitigates the ill-conditioning of the differentiation matrix and enhances numerical stability. The influence of the trust-region radius on the descent behavior of the penalty function is then analyzed, leading to a two-phase optimization strategy. In phase I, line-search is adopted to rapidly obtain a feasible solution, thereby avoiding convergence delays caused by prematurely large penalty coefficients. In phase II, the step size is finely tuned using trust-region constraints, ensuring both convergence accuracy and improved robustness. Numerical simulations demonstrate that the proposed algorithm achieves superior performance in terms of convergence speed and solution accuracy, while effectively eliminating the numerical oscillations typically induced by excessive penalty coefficients in conventional penalty methods. The framework provides a reliable and efficient numerical approach for reentry trajectory optimization.

For deep space missions, optimizing flyby sequences is essential to reach target destinations with sufficient fuel for scientific objectives. However, identifying optimal flyby sequences is challenging. Conventional global optimization algorithms typically demand a fixed number of design variables, which constrains their applicability for such complex problems. Even with a predefined number of flybys, the extensive design space makes the optimization process computationally expensive. To address this issue, the current study proposes a vectorized dynamic-size genetic algorithm (VDSGA) designed to rapidly generate optimal flyby sequences. To further enhance computational efficiency, the proposed VDSGA method is integrated with a neural network Lambert’s approximator (NNLA), enabling high-speed, accurate approximations of Lambert’s problem. This incorporation improves computational efficiency by leveraging GPU-assisted trajectory optimization without the need for implementation-level modifications, thereby reducing reliance on CPU processing. This approach also provides several different suboptimal flyby sequences, thus broadening the solution space for a given transfer. The accuracy and feasibility of these preliminary design solutions are validated through interplanetary mission examples in this paper.

Homotopy methods have received increasing attention for their effectiveness in solving nonlinear optimal control problems, particularly in the aerospace domain. This review provides a comprehensive survey of the development and application of homotopy methods for trajectory optimization, with a particular emphasis on recent advances. The methodological evolution and theoretical foundations of homotopy are first outlined. Subsequently, homotopy methods within the optimal control framework are presented according to the indirect and direct approaches. Based on the scope of influence of the homotopy mapping, these methods are further categorized into locally structured and globally structured approaches, with their respective characteristics emphasized. Finally, the key open challenges are synthesized to elucidate the inherent limitations of current homotopy approaches and to highlight research directions that may further unlock their potential in aerospace trajectory design and optimization.

Satellite orbit decision-making requires a reliable approach in uncertain environments. Classical guidance methods can produce high-quality trajectories but rely on repeated numerical solutions of boundary-value problems, which limits their scalability for large-scale online decision-making. Deep reinforcement learning (RL) can in principle handle nonlinear high-dimensional dynamics, yet its interaction demand makes sample efficiency a central bottleneck for orbital decision-making under high-fidelity astrodynamics models. This paper proposes an RL framework for learning-efficient orbit decision based on an astrodynamics-informed parameterized action space. Each action consists of a discrete maneuver index and a continuous parameter vector. The discrete index selects a maneuver primitive from a library of classical orbital guidance methods. The continuous parameters unify the internal degrees of freedom of these primitives. This architecture with reparameterized continuous sampling and projection enforces maneuver feasibility and generates bounded physically meaningful commands. Comprehensive simulations demonstrate that the proposed astrodynamics-informed parameterization improves sample efficiency and final performance compared with unstructured Δv action spaces and normalized baselines.

In space situational awareness (SSA), non-cooperative targets are monitored under complex illumination and dynamic conditions, where reliable quantitative observation evaluation is essential for threat detection and catalog maintenance. This paper addresses the challenge of constructing a space-based observation evaluation framework for non-cooperative targets under complex illumination and dynamic conditions, and develops an artificial neural network (ANN) algorithm for optimizing comprehensive observation effectiveness-evaluation parameters within a typical rendezvous and proximity operations (RPO) dynamics framework. The study integrates constraints such as solar incidence angle, field of view, and relative angular rate, forming a multi-constraint finite-horizon set. A comprehensive effectiveness-evaluation model is proposed, incorporating submodels for relative distance, image motion, effective observation time, and coverage, with dynamic weighting based on mission priority. The comprehensive observation effectiveness evaluation problem is formulated as a highly nonlinear optimization task, and an improved reinforcement learning neural network algorithm with feedback coefficient scheduling (FCS-RLNNA) is introduced. Numerical simulations in GEO scenarios demonstrate that FCS-RLNNA outperforms existing optimization methods in terms of convergence speed and solution stability. The proposed framework effectively enhances multi-constraint RPO observation while satisfying safety and imaging-quality requirements.

Using low-thrust engines to change the orbit of a near-Earth spacecraft reduces the fuel required for the maneuver due to their high specific impulse. However, the smallness of the thrust relative to the Earth’s gravity leads to long flight times and spiral trajectories, the design of which requires advanced methods. The finite-Fourier method of approximating the periodic thrust acceleration allows one to find a control that implements a quasi-optimal low-thrust flight in terms of fuel consumption in the model of an ideally regulated thrust engine. In this paper, the finite-Fourier approach is generalized to a class of non-periodic controls by introducing the time dependence of the Fourier coefficients of the thrust acceleration. It is shown that the expansion of the control function class leads to a decrease in fuel costs. The trajectories obtained by this approach are useful in the design of flights in near-Earth space, since they can be used to estimate fuel costs in preliminary mission analysis. They can also serve as an initial guess for the problems of constructing the fuel-optimal control in the class of piecewise smooth functions.

For the space antennas featuring the novel double-ring truss structures, the equivalent dynamic modeling serves as one of the key prerequisites for the design of vibration control strategies. In comparison to the conventional planar truss structures, the double-loop trusses exhibit greater complexity by connecting multiple periodic beam units through rigid joints and cable-driven mechanisms. This study proposed a more concise equivalent anisotropic Timoshenko beam model based on the energy equivalence principle and a static condensation method, under the condition of applying initial stress to enhance structural stiffness. The relationship between geometric parameters and material parameters was established by considering the displacement expressions of the center points of periodic elements. Subsequently, the stiffness matrix and mass matrix of the entire structure were obtained by connecting multiple equivalent elements along the circumference. Verification of the vibration characteristics of the proposed equivalent dynamic model was achieved by comparing the modal tests, full-scale finite element simulations, and numerical solutions. Simulation results indicate that in single basic unit modal tests, the maximum error for low-order structural frequencies is approximately 3.8%. For a dual-ring truss structure with 25 m diameter, the variations in the first eight natural frequencies under different pre-stress conditions are discussed in detail. The relative error of modal frequencies is roughly within 2.5%. Furthermore, the low-order frequencies of structures with 50 m and 100 m diameters are calculated, with the proposed equivalent dynamic model achieving computational times only 1/10 of those required by the finite element model. The model demonstrated good accuracy and broad engineering applicability.