Xiang Zhang

Thin-walled carbon fiber-reinforced polymer (CFRP) members with closed lenticular cross-sections are suitable for lightweight load-bearing and deployable aerospace structures. Their axial buckling may be governed by competition between finite-length local or cross-sectional deformation branches and long-member global flexural branches. This competition increases the computational cost of parametric sweeps and complicates rapid identification of the transition length. This study develops a combined Rayleigh–Ritz–Euler screening framework to address this challenge. A finite-length Rayleigh–Ritz (RR) model predicts local and mixed buckling branches and the governing half-wave number, while an Euler model provides the global flexural buckling load scale. The intersection of the predicted load–length curves defines an engineering transition length, and shell finite element (FE) analyses are used to check selected configurations. The RR model incorporates an effective closure coefficient for the geometric stiffness, calibrated solely on the baseline configuration (BASE). Against Abaqus linear buckling results, excluding BASE, RR and Euler yield root-mean-square relative errors of 11.6% and 1.3% in the local and global buckling regimes, respectively. Across all 13 configurations, including BASE, the engineering transition length has a mean absolute relative error of 2.4% and a maximum of 11.1%. The framework captures transition-length trends under the investigated variations in cross-sectional scale, thickness, and layup, but shows larger deviations for variations in the circular-arc subtended angle. Within the tested parameter range, it supports preliminary screening of candidate designs and identification of parameter regions for targeted FE verification. However, it cannot replace high-fidelity FE analysis or experiments as the basis for final design decisions.

The time-optimal rendezvous problem is crucial for efficiently executing on-orbit servicing (OOS) missions in the future. To fulfill the detection requirement during rendezvous process, it is an essential issue that the maneuvering spacecraft flies over the designated waypoint. This paper presents an innovative methodology for planning the time-optimal spacecraft rendezvous trajectory, involving the constraints related to a flyover waypoint and being forced by a constant thrust. The method is specifically designed to handle the optimal problems with the shortest and unspecified flyover time and terminal rendezvous time. First, this article outlines the scenarios for a time-optimal rendezvous that incorporates the constraints of a flyover waypoint. Second, a time-normalized relative dynamic model for maneuvering spacecraft is derived using the Clohessy–Wiltshire (CW) equation. Third, the time-optimal control output under the constant thrust is provided leveraging Pontryagin’s minimum principle (PMP). Meanwhile, an indirect solution equation is established with the constraints of relative position and velocity for the flyover waypoint during the rendezvous process. Finally, a computational methodology for solving this time-optimal problem is proposed, integrating the initial guess for the unspecified time, multi-objective particle swarm optimization using multiple search strategies (MMOPSO) and Newton–Raphson method (NRM). Simulation results demonstrate that the method can effectively and practically solve the time-optimal rendezvous trajectory planning under a constant thrust, while satisfying the constraints of the flyover waypoint. Moreover, Monte Carlo simulations are performed, the results of which indicate that the proposed methodology exhibits strong robustness and fidelity.

The component-level line of sight (LOS) angle measurement of spacecraft is much desired during space rendezvous, especially for component-related operations, such as component status evaluation, component repair, etc. However, most existing methods rarely consider the component approaching scenario where a continuous, stable, real-time LOS angle measurement method for the component of interest is needed. In this paper, a continuous robust component-level LOS angle measurement method with high computational efficiency applicable to the approach of the key component is proposed. Firstly, an adaptive gamma correction method is introduced to enhance the image quality in complex and variable lighting environments. Secondly, optimized thresholding that exploits information entropy is proposed to identify the pixels that are supposed to be the target from the background. Region detection is subsequently performed to segment the target region into suspected component regions, which can account for target changes during the approach by seamless parameter adaptation. Then, solar panels are recognized and accurately segmented based on the prior knowledge of their spatial relationship with other components and unique shape features. Finally, the centers of solar panels are localized and their LOS angles are calculated. Extensive experiments are conducted to demonstrate the performance of our proposed method, including the verification of the superiority of the solar panel recognition and segmentation method using both simulated images generated by an image simulator and actual images taken by a camera in a dark-room considering the actual lighting in space, and the validation of the ability of supporting real-time component-level LOS angle measurement by ground semi-physical experiments with a guidance, navigation and control (GNC) system incorporated to simulate an on-line dynamic approach.

A cooperative game negotiation strategy considering multiple constraints is proposed for distributed impulsive multi-spacecraft approach missions in the presence of defending spacecraft. It is a dual-stage decision-making method that includes offline trajectory planning and online distributed negotiation. In the trajectory planning stage, a relative orbital dynamics model is first established based on the Clohessy–Wiltshire (CW) equations, and the state transition equations for impulsive maneuvers are derived. Subsequently, a multi-objective optimization model is formulated based on the NSGA-II algorithm, utilizing a constraint dominance principle (CDP) to address various constraints and generate Pareto front solutions for each spacecraft. In the distributed negotiation stage, the negotiation strategy among spacecraft is modeled as a cooperative game. A potential function is constructed to further analyze the existence and global convergence of Nash equilibrium. Additionally, a simulated annealing negotiation strategy is developed to iteratively select the optimal comprehensive approach strategy from the Pareto fronts. Simulation results demonstrate that the proposed method effectively optimizes approach trajectories for multi-spacecraft under complex constraints. By leveraging inter-satellite iterative negotiation, the method converges to a Nash equilibrium. Additionally, the simulated annealing negotiation strategy enhances global search performance, avoiding entrapment in local optima. Finally, the effectiveness and robustness of the dual-stage decision-making method were further demonstrated through Monte Carlo simulations.

To address the issue of misclassification and diminished accuracy that is prevalent in existing intent recognition models for non-cooperative spacecraft due to the omission of environmental influences, this paper presents a novel recognition framework leveraging a hybrid neural network subject to multiple constraints. The relative orbital motion of the targets is characterized and categorized through the use of Clohessy–Wiltshire equations, forming the foundation of a constrained intention dataset employed for training and evaluation. Furthermore, the method incorporates a composite architecture combining a convolutional neural network (CNN), long short-term memory (LSTM) unit, and self-attention (SA) mechanism to enhance recognition performance. The experimental results demonstrate that the integrated CNN-LSTM-SA model attains a recognition accuracy of 98.6%, significantly surpassing traditional methods and neural network models. Additionally, it demonstrates high efficiency, indicating significant promise for practical applications in avoiding spacecraft collisions and performing orbital maneuvers.

The Very Low Earth Orbit (VLEO) Satellite, Extremely Low-Earth Imaging and Technology Explorer (ELITE), intends to find out the lowest possible altitudes it can explore. In doing so, the significantly larger atmospheric drag expected necessitates much higher levels of thrust to counteract, resulting in extremely high power consumption levels. Furthermore, increasingly prevalent uncertainties and disturbances will affect the accuracy of ELITE's attitude control, which is complex and computationally intensive from strong nonlinearity. These factors are expected to worsen as ELITE descends to lower altitudes. To address these factors, we developed the Robust Multiplexed MPC (RMMPC) that uses our Cooperative Distributed Piecewise Affine model (CDPWA). This model simplifies complex nonlinear attitude dynamics into computationally manageable highly accurate linear models, having different control periods for ELITE's hybrid actuator system to further reduce computational complexity. The coordinated distributed aspect of the model enables the controller to coordinate all control inputs to achieve three-axis control. These control inputs are “multiplexed”, being updated individually instead of simultaneously, exploiting the resulting computational simplicity. An observer will be integrated to account for the increasing uncertainties and disturbances, enhancing controller robustness. Additionally, we propose the synchronization of sun tracking with thruster firing and target pointing operations, using Beta-distribution Differential Evolution (BDE) to optimize the power consumption during these operations, intending to maximize solar power generation. Hardware-in-the-Loop (HIL) experimental results certify the effectiveness of these proposals in enabling ELITE to meet the required operational capabilities in VLEO Emergency Mode.

Active space debris removal is now integral to modern space exploration. In order to address the problem of a heterogeneous satellite swarm with different payloads carrying out the emergency active removal of space debris, this paper proposes a Multi-type Chromosome Fast Elitist Non-Dominated Sorting Genetic Algorithm (MC-NSGA-II). The algorithm is designed to enable the satellite swarm to execute multiple coupled tasks in succession with improved optimization efficiency. An arbitrary execution order may result in deadlock, where one or more satellites become trapped in an infinite waiting loop. In order to address the heterogeneous problem of satellites and task coupling constraints, a multi-type chromosome coding strategy is developed. To evaluate different allocation strategies, three optimization objectives—time consumption, fuel consumption, and task balance—are introduced. To align with the multi-type chromosome coding strategy, two distinct sorting methods are developed for crossover and mutation operations, ensuring that all offspring individuals meet the constraints. Additionally, the algorithm incorporates a dynamic parameter-setting strategy to enhance solution efficiency. Finally, comparative simulations validate the effectiveness and superiority of the proposed method. The results show that the high-quality solution search ability of the MC-NSGA-II algorithm is 23.07% higher than that of the standard NSGA-II algorithm.

A circularly polarized (CP) patch antenna array with an oblique beam is proposed, which can effectively broaden the satelliteground communication link area as the satellite rotates mechanically. The array consists of 6 subarrays with 12 antenna elements, covering the typical L1 frequency band of global navigation satellite systems (GNSS) from 1.544 GHz to 1.615 GHz. Three main issues are mainly addressed, including the array axis ratio (AR) bandwidth enhancement, the array tilted beam design and the environment suitability in aerospace. The sequential feeding with 90° phase shift is preferred in the subarray to improve the 3-dB AR bandwidth. To realize a 30° oblique radiated beam with specified gains, the trust region framework aided beamforming technique is applied to optimize the phase differences between adjacent subarrays. The high temperature alumina ceramic and WL-CT300 dielectric substrates are integrated to extend the array working temperature from -50°C to 260°C. Both simulations and measurements are conducted. It is demonstrated that the maximum realized gain is 10.56 dBi at 1.575 GHz, and the 3-dB axis ratio bandwidth (ARBW) is 110 MHz or 7% in fraction. The proposed CP patch antenna array shows excellent performance in terms of high gain, wide ARBW, lightweight and environment suitability, making it a promising candidate for satellite-wide scanning coverage applications.

A cooperative steering control algorithm based on external disturbances and actuator faults can effectively improve the lateral stability of intelligent vehicles. In order to capture the nonlinear and uncertain characteristics of the vehicle system, such as the tire lateral cornering stiffness, varying speeds, and uncertainty in the driver's steering behavior, an interval type-2 fuzzy method is used to model the system. Furthermore, actuator faults are considered within the control structure, and a fault-tolerant control project is integrated into the cooperative steering controller to ensure a stable output of the assisted steering. Then, a set of sufficient conditions are given to calculate the gain of the cooperative steering controller by utilizing the theory of Lyapunov function, and external disturbances and actuator faults problem are considered to improve the robustness and reliability of the controller. Finally, simulation tests are conducted on the Matlab/Simulink platform. The results show that the proposed method significantly enhances the lateral dynamic stability of intelligent vehicles.

This paper proposes an optimal maneuver strategy to improve the observability of angles-only rendezvous from the perspective of relative navigation. A set of dimensionless relative orbital elements (ROEs) is used to parameterize the relative motion, and the objective function of the observability of angles-only navigation is established. An analytical solution of the optimal maneuver strategy to improve the observability of angles-only navigation is obtained by means of numerical analysis. A set of dedicated semi-physical simulation system is built to test the performances of the proposed optimal maneuver strategy. Finally, the effectiveness of the method proposed in this paper is verified through the comparative analysis of the objective function of the observability of angles-only navigation and the performances of the angles-only navigation filter under different maneuver schemes. Compared with the cases without orbital maneuver, it is concluded that the tangential filtering accuracy with the optimal orbital maneuver at the terminal time is increased by 35% on average, and the radial and normal filtering accuracy is increased by 30% on average.