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This study examines how a spacecraft reacts to constant body-fixed torques and a gyrostatic moment (GM), as well as the impact of energy dissipation. The spacecraft model being studied includes a spherical slug near the center of mass covered by a viscid layer. The problem’s difficulty lies in solving its governing equations of motion (EOMs), which are derived through Euler nonlinear equations. Understanding the behavior of this model can offer insights into how spacecraft respond to external torques, aiding in the development of more efficient and stable systems for aerospace and robotics applications. The research delves into the relationship between energy dissipation and GM on the spacecraft motion in three different scenarios involving constant torques around three various axes. Detailed analysis, as well as novel solution and simulation results, are presented for different energy dissipation possibilities. The influence of manipulating the value of the GM and the viscosity of the layer has been approached. These findings are crucial for comprehending, maintaining, and controlling the motion of spacecraft influenced by external forces in space. The study promises to have a significant impact on the aerospace industry, particularly in the design and operation of spaceships and satellites, by enhancing our knowledge of rotational motion and celestial bodies’ behavior. A comprehensive report will be produced to elucidate the complexities of rotational and orbital motion discovered during this research.

Spacecraft are subjected to various external loads during flight, and these loads have a direct impact on the structural safety and functional stability of the spacecraft. Obtaining external load information can provide reliable support for spacecraft health detection and fault warning, so accurate load identification is very important for spacecraft. Compared with the traditional time-domain load identification method, the neural network-based time-domain load identification method can avoid the establishment of the inverse model and realize the response-load time-sequence mapping, which has a broad application prospect. In this paper, a CNN-LSTM-SA neural network-based load identification method is proposed for load acquisition of a thin-walled spacecraft model. Simulation results show that the method has higher identification accuracy and robustness (RMSE and MAE of 8.47 and 10.83, respectively, at a 20% noise level) in the load identification task compared to other network structures. The experimental results show that the coefficients of determination (R2) of the proposed neural network load recognition model for time-domain identification tasks of sinusoidal and random loads are 0.98 and 0.93, respectively, indicating excellent fitting performance. This study provides a reliable new method for load identification in thin-walled spacecraft cabin structures.

This paper addresses the challenge of robust pose estimation for spacecraft under rapid inter-frame motion, proposing a two-stage point cloud registration framework. The first stage computes coarse pose estimation by leveraging Fast Point Feature Histogram (FPFH) descriptors with random sample and consensus (RANSAC) for correspondence matching, effectively handling significant positional displacements. The second stage refines the solution through geometry-aware fine registration using raw point cloud data, enhancing precision through a multi-scale iterative ICP-like framework. To validate the approach, we simulate time-of-flight (ToF) sensor measurements by rendering NASA’s public 3D spacecraft models and obtain 3D point clouds by back-projecting the depth measurements to 3D space. Comprehensive experiments demonstrate superior performance over several state-of-the-art methods in both accuracy and robustness under rapid inter-frame motion scenarios. The dual-stage architecture proves effective in maintaining tracking continuity while mitigating error accumulation from fast relative motion, showing promise for autonomous spacecraft proximity operations.

A novel combination of finite time control and control allocation with uncertain configuration matrix due to actuator misalignment is investigated for attitude stabilization of a rigid spacecraft. Finite time controller using nonsingular terminal sliding mode technique is firstly designed as virtual control of control allocator to produce the three axis torques, and can guarantee finite time reachability of given attitude motion of spacecraft in the presence of external disturbances. The convergences of this feedback controller for the resulting closed loop systems are also proven theoretically. Then, under the condition of uncertainty included in the configuration matrix due to actuator misalignment, a robust least squares-based control allocation is employed to deal with the problem of distributing the three axis torques over the available actuators under redundancy, in which the focus of this control allocation is to find the optimal control vector of actuator by minimizing the worst-case residual, under the condition of the uncertainty included in actuator configuration matrix and control constraints like saturation. Simulation results using the orbiting spacecraft model show good performance under external disturbances and even uncertain configuration matrix, which validates the effectiveness and feasibility of the proposed scheme.

In this work, the large-angle rotational movement and vibration suppression of a flexible spacecraft are carried out based on an adjustable system. First the spacecraft model is transformed into a canonical affine control form, then two fuzzy systems are used: The first (of Takagi–Sugeno type) estimates the feedback linearization control law as a whole, while the second (of Mamdani type) adjusts and stabilizes the control parameters using the gradient descent technique and based on the minimization of the control error rather than the tracking error. Stability results are presented in terms of Lyapunov’s theory, and simulation tests illustrate the significant transient robustness of the closed-loop system against perturbations, the accurate trajectory control, and vibration suppression of the flexible spacecraft. Consequently, as will be shown later, the error will stay confined and converges quickly to zero, confirming the smoothing property of the proposed method using fuzzy logic systems.

This paper carries on a reliability sensitivity analysis on the non-ablative thermal protection system (TPS) of spacecraft during the conceptual design. In the previous work on probabilistic estimation of TPS, the temperature dependency of material properties has not yet been investigated. In this paper, however, the temperature dependency of material properties is characterized and considered during the thermal analysis and reliability sensitivity analysis. Compared to general black-box problems, three special challenges of uncertainty analysis for TPS in real practice are a generally high dimension and multiple outputs on massive meshing nodes, a high level of reliability design target, and a fast evaluation process due to the requirement of the conceptual design. In order to cope with these challenges, a unified reliability sensitivity analysis methodology including multi-input and multi-output support vector machines (MIMO-SVMs), a space-partition (SP) method, and a generalized subset simulation (GSS) is proposed for the conceptual design of TPS with temperature-dependent materials. MIMO-SVMs are used to approximate the thermal responses to save calculation costs. The variance-based global sensitivity indices are calculated by SP to make full use of the information within samples. Based on the sensitivity indices, a dimension reduction process is introduced. In the reduced space, GSS is used to simultaneously evaluate all the failure probabilities by fully exploring the correlation among all the LSFs. Two application examples including a lifting body vehicle model and a spacecraft model are used to demonstrate the performance of the proposed methodology.

Coupling between the rotational and translational motion of a rigid body can have a profound effect on spacecraft motion in complex dynamical environments. While there is a substantial amount of study of rigid-body coupling in a non-uniform gravitational field, the spacecraft is often considered as a point-mass vehicle. By contrast, the full-N body problem (FNBP) evaluates the mutual gravitational potential of the rigid-body celestial objects and any other body, such as a spacecraft, under their influence and treats all bodies, including the spacecraft, as a rigid body. Furthermore, the perturbing effects of the FNBP become more pronounced as the celestial bodies become smaller and/or more significantly aspherical. Utilizing the comprehensive framework of dynamics and gravitational influences within the FNBP, this research investigates the dynamics of spacecraft modeled as rigid bodies in binary systems characterized by nearly circular mutual orbits. The paper presents an examination of the perturbation effects that arise in this circular restricted full three-body problem (CRF3BP), aiming to assess and validate the extent of these effects on the spacecraft’s overall motion. Numerical results provided for spacecraft motion in the CRF3BP in a binary asteroid system demonstrate non-negligible trajectory divergence when utilizing rigid-body versus point mass spacecraft models. These results also investigate the effects of shape and inertia tensors of the bodies and solar radiation pressure in those models.

Flexible surface acoustic wave technology has garnered significant attention for wearable electronics and sensing applications. However, the mechanical strains induced by random deformation of these flexible SAWs during sensing often significantly alter the specific sensing signals, causing critical issues such as inconsistency of the sensing results on a curved/flexible surface. To address this challenge, we first developed high-performance AlScN piezoelectric film-based flexible SAW sensors, investigated their response characteristics both theoretically and experimentally under various bending strains and UV illumination conditions, and achieved a high UV sensitivity of 1.71 KHz/(mW/cm²). To ensure reliable and consistent UV detection and eliminate the interference of bending strain on SAW sensors, we proposed using key features within the response signals of a single flexible SAW device to establish a regression model based on machine learning algorithms for precise UV detection under dynamic strain disturbances, successfully decoupling the interference of bending strain from target UV detection. The results indicate that under strain interferences from 0 to 1160 με the model based on the extreme gradient boosting algorithm exhibits optimal UV prediction performance. As a demonstration for practical applications, flexible SAW sensors were adhered to four different locations on spacecraft model surfaces, including flat and three curved surfaces with radii of curvature of 14.5, 11.5, and 5.8 cm. These flexible SAW sensors demonstrated high reliability and consistency in terms of UV sensing performance under random bending conditions, with results consistent with those on a flat surface.

Recent efforts in on-orbit servicing, manufacturing, and debris removal have accentuated some of the challenges related to close-proximity space manipulation. Orbital debris threatens future space endeavors driving active removal missions. Additionally, refueling missions have become increasingly viable to prolong satellite life and mitigate future debris generation. The ability to capture cooperative and non-cooperative spacecraft is an essential step for refueling or removal missions. In close-proximity capture, collision avoidance remains a challenge during trajectory planning for space manipulators. In this research, a deep reinforcement learning control approach is applied to a three-degrees-of-freedom manipulator to capture space objects and avoid collisions. This approach is investigated in both free-flying and free-floating scenarios, where the target object is either cooperative or non-cooperative. A deep reinforcement learning controller is trained for each scenario to effectively reach a target capture location on a simulated spacecraft model while avoiding collisions. Collisions between the base spacecraft and the target spacecraft are avoided in the planned manipulator trajectories. The trained model is tested for each scenario and the results for the manipulator and base motion are detailed and discussed.

Modern spacecraft are often equipped with large-scale, complex, and lightweight solar arrays whose deployment involves a highly dynamic movement. This paper proposed a novel adaptive proportional-derivative typed fuzzy logic control scheme for the attitude stabilization of a flexible spacecraft during the deployment of a composite laminated solar array. First, a constrained rigid-flexible coupling spacecraft model consisting of a rigid main body and a flexible solar array was proposed. The solar array, which is composed of composite laminated shells, was described by the absolute nodal coordinate formulation. Then, the detailed derivation of the adaptive fuzzy PD controller for attitude stabilization of the spacecraft was discussed. In addition, the spacecraft dynamic model which integrated the adaptive fuzzy PD controller was derived as a set of differential-algebraic equations. Several simulations were developed to investigate the solar array deployment dynamics and to verify the effectiveness of the proposed adaptive fuzzy PD controller. The results suggested that the proposed dynamic model is able to exactly describe the deployment dynamics of the composite laminated solar array. The solar array deployment causes obvious translational and rotational motions of the spacecraft. The proposed adaptive fuzzy PD control scheme has better performance in terms of the control precision and time response in stabilizing spacecraft during the deployment of the composite laminated solar array, comparing with that of the conventional PD controller.