Mars aerocapture is one of the most concerned technologies for future Mars sample-return and manned exploration missions. This article investigates the challenging problem of the reference trajectory tracking guidance for Mars aerocapture under uncertainties. Based on an integral sliding surface, a fixed-time neural adaptive tracking guidance law is synthesized by incorporating the neural network (NN) approximation into the fixed-time integral sliding mode control approach. Benefiting from the integral sliding surface design, the proposed tracking guidance law has no singularity problem inherently existing in terminal sliding mode control. By adopting the NN approximation to compensate for the lumped uncertain term in the feedforward loop, the proposed tracking guidance law is strongly robust against aerodynamic coefficient uncertainties and atmospheric density uncertainty. Stability analysis shows that the radial distance tracking error and its time derivative can stabilize to the small neighborhoods around the origin in fixed time under the proposed tracking guidance law. Finally, the effectiveness and advantages of the proposed tracking guidance law are illustrated through simulations and comparisons.

Aerocapture is a flight maneuver that achieves orbit insertion around a planet by using the aerodynamic drag generated by the atmosphere of the planet to decelerate. Trajectory planning, which prov...

Spacecraft component detection is essential for space missions, such as for rendezvous and on-orbit assembly. Traditional intelligent detection algorithms suffer from drawbacks related to high computational burden, and are not applicable for on-board use. This paper proposes a convolutional neural network (CNN)-based lightweight algorithm for spacecraft component detection. A lightweight approach based on the Ghost module and channel compression is first presented to decrease the amount of processing and data storage required by the detection algorithm. To improve feature extraction, we analyze the characteristics of spacecraft imagery, and multi-head self-attention is used. In addition, a weighted bidirectional feature pyramid network is incorporated into the algorithm to increase precision. Numerical simulations show that the proposed method can drastically reduce the computational overhead while still guaranteeing good detection precision.

Orbit uncertainty propagation plays an important role in the analysis of a space mission. The accuracy and computation expense are two critical essences of uncertainty propagation. Repeated evaluations of the objective model are required to improve the preciseness of prediction, especially for long-term propagation. To balance the computational complexity and accuracy, an adaptive Gaussian mixture model using virtual sample generation (AGMM-VSG) is proposed. First, an unscented transformation and Cubature rule (UT-CR) based splitting method is employed to adaptive update the weights of Gaussian components for nonlinear dynamics. The Gaussian mixture model (GMM) approximation is applied to better approximate the original probability density function. Second, instead of the pure expensive evaluations by conventional GMM methods, virtual samples are generated using a new active-sampling-based Kriging (AS-KRG) method to improve the propagation efficiency. Three cases of uncertain orbital dynamical systems are used to verify the accuracy and efficiency of the proposed manuscript. The likelihood agreement measure (LAM) criterion and the number of expense evaluations prove the performance.