This paper investigates the attitude stabilization control problem with low-frequency communication to actuators in the framework of sampled-data control. A novel event-triggered sampling control policy is proposed by employing an integral-type triggering function where the determination of all sampling times is by judging the properties of this function including the integration of measurement errors. Using the Lyapunov-based approach, we show that the stability of the closed-loop system can be guaranteed in the presence of external disturbance, inertia uncertainty, and actuator fault. Compared with conventional attitude control policies, the proposed algorithm significantly reduces the data-rate requirement in updating the actuator while providing high reliability and accurate performance for attitude stabilization. Compared with the traditional event-triggered sampling, the proposed policy is no longer by judging the instantaneous state of measurement errors, which reduces the sampling frequency and does not increase the computational burden. Numerical simulations are conducted to show a decent performance of the algorithm.

Space-based gravitational wave detection requires a strict level of residual acceleration ( $\sim$ $10^{-15} \mathrm{~m/s}^{2} / \sqrt{\text{Hz}}$ in the frequency band of $0.1\text{mHz}\sim 1\text{Hz}$ ) on the test masses along the sensitive axes. Because of the configuration of the detection spacecraft with two test masses, there are complex couplings between the spacecraft attitude, drag-free and suspension control loops in the drag-free and attitude control system. Moreover, the space environment disturbances and the limited noise levels of the actuators and sensors make it difficult for multiple control degrees of freedom to meet the index requirements at the same time. Currently, PID and $H_\infty$ methods are mainly used for its controller design, where the system is decoupled and divided into relatively independent control loops, and the controllers are designed to meet the requirements separately. However, parameter tuning usually requires empirical trial and error, leading to the low design efficiency. And the coupling relationships between loops cannot be well described and treated mathematically. Therefore, a controller design method for drag-free spacecraft multi loops is proposed in this paper, where the index requirements are transformed into frequency domain constraints and the optimization objectives are designed for better performances according to the control characteristics of different loops. Then the controllers are tuned by multi-population genetic algorithm improving the design efficiency. As mentioned, under the premise that the index requirements are all met, the couplings between loops are considered and some of the performances of the system can be optimized, such as the residual acceleration acting on the test masses. Numerical simulation and comparison verify the effectiveness of this method.

With the continuous advancement of deep space exploration missions, the solar system boundary exploration mission is established as one of the China's most important deep space scientific exploration missions. The mission of the solar system boundary exploration has many challenges such as ultra-remote detection distance, ultra-long operation time, and ultra-long communication delay. Therefore, the problem of high-precision autonomous navigation needs to be solved urgently. This paper designs an autonomous intelligent navigation method based on X-ray pulsars in the cruise phase, which estimate the motion state of the probe in real time. The proposed navigation method employs the Q-learning Extended Kalman filter (QLEKF) to improve navigation accuracy during long periods of self-determining running. The QLEKF selects automatically the error covariance matrix parameter of the process noise and the measurement noise by the reward mechanism of reinforcement learning. Compared to the traditional EKF and AEKF, the QLEKF improves the estimation accuracy of position and velocity. Finally, the simulation result demonstrates the effectiveness and the superiority of the intelligent navigation algorithm based on QLEKF, which can satisfy the high-precision navigation requirements in the cruise phase of the solar system boundary exploration.

Space-based gravitational wave detecting satellite's drag-free control system contains two test masses (TMs), and the relative translational motion between the three is coupled. Unlike the conventional single TM drag-free satellite, the double TMs drag-free satellite in gravitational wave detection cannot track the two TMs simultaneously with six relative translational degrees of freedom. Therefore, a suitable drag-free tracking approach is necessary to be chosen between the satellite and the two TMs for meeting the residual acceleration demand in the direction of the TMs’ sensitivity axis. Different drag-free tracking techniques correspond to various decoupling feedback techniques, which result in various control performances between two TMs and a corresponding impact on fuel consumption. First, a new virtual point tracking approach that offers several possibilities for choosing drag-free reference points is provided after analyzing the virtual point tracking strategy used by the double TMs gravitational wave detection satellite. The position of the fuel optimal reference point is then determined. Additionally, the proposed tracking approach can reduce fuel consumption by about 15.8% at the fuel optimal reference point, and the results of the numerical simulations demonstrate excellent agreement with our hypothesis. The suggested tracking approach incorporates the mission requirements and fuel consumption, which can reduce fuel consumption and significantly lengthen the time of the satellite detection mission while maintaining the mission demands. This is in contrast to the current tracking technique.

This paper proposes an energy- and size-aware clustering (ESC) strategy for megaconstellation systems. The methodology constructs a hierarchical architecture with designated cluster heads and member satellites through role differentiation. Cluster heads serve as relay nodes, reducing the communication load on ground infrastructure by interacting with ground stations and other entities. This hierarchical approach enables efficient constellation control through interactions primarily with cluster heads. The ESC strategy begins with a cluster head election process, incorporating orphan handling management to select cluster heads, ensuring full coverage of the entire constellation. Then, a two-stage multi-objective optimization framework is used to deal with division of members to clusters and intra-cluster multi-hop connectivity configurations. This dual-phase approach optimizes energy consumption while maintaining balanced cluster sizes. Simulations on the OneWeb system validate the ESC strategy as a viable solution in that it achieves 55.9% energy consumption reduction compared to traditional discrete gradient optimization methods while maintaining more balanced cluster sizes.