This paper focuses on simultaneous estimation of states and faults for a linear time-invariant (LTI) system observed by sensor networks. Each sensor node is equipped with an observer, which uses only local measurements and local interaction with neighbors for monitoring. The observability of said observer is analyzed where non-local observability of a sensor node is required in terms of the system state and faults. The distributed observers present features of $H_{\infty }$ performance to constrain the influence of disturbances on the estimation errors, for which the global design condition is transformed into a linear matrix inequality (LMI). The LMI is proven to be solvable given collective observability of the system and a suitable $H_{\infty }$ performance index. Moreover, in the case that no disturbances exist, fully distributed observers with adaptive gains are designed to asymptotically estimate the states and faults without using any global information from the network. Finally, the effectiveness of the proposed methods is verified through case studies on a spacecraft’s attitude control system.

This paper investigates the distributed optimal cooperative tracking control problem for multi-input linear time-invariant (LTI) systems. In this context, the system inputs are generated by a group of agents that communicate with each other over a network, i.e., each control input channel is considered as an agent, which can communicate over a network to transmit information and compute control input. Unlike centralized optimal tracking control, where inputs are designed using global information, each agent in the distributed framework has access only to its own input matrix and communicates solely with its neighbors within the network. This limitation introduces significant challenges in designing the optimal controller. To address this issue, an information fusion method is first proposed, enabling each agent to derive its optimal controller in a distributed manner. For scenarios where the system model is unknown, a fusion-based learning algorithm is further developed. The convergence and optimality of this algorithm are rigorously proved. A simulation example is provided to illustrate the effectiveness of the proposed approach.

The sealing ring of the heating furnace in the volatiles measurement instrument is prone to contamination by lunar dust during on-orbit use, affecting its performance. Therefore, a static sealing spring-energized sealing ring was designed which is resistant to high and low temperatures, capable of multiple repeated seals, and possesses good dust tolerance. In order to verify the stable sealing performance of the sealing ring under moon dust contamination, a test platform was constructed to evaluate the changes in sealing performance after contamination by simulated lunar dust. The results show that under extreme conditions, with a sealing force of only 300 N, the leakage rate was less than 9 × 10^–4 Pa∙m³/s, increasing sealing force was to be the most effective way to reduce leakage rate, and within an appropriate range of sealing force, the sealing ring was capable of multiple repeated seals. This study provides a reference for on-orbit application of the lunar soil volatiles measurement instrument.