Orbital edge computing (OEC) is crucial for supporting space intelligence applications within satellite networks. However, individual satellites face resource constraints, and implementing distributed processing techniques, such as federated learning (FL), across multiple satellites introduces significant scheduling complexity. To address these challenges, we first model the key factors influencing complex satellite networks, including satellite constellations, regional resource demands, inter-satellite communication and routing, energy consumption, and battery aging—a novel aspect invoked by OEC operations. We propose an adaptive aggregation method to fundamentally improve communication efficiency in OEC-based FL. To enhance scheduling performance, we formulate a unified optimization problem that jointly considers data partitioning, resource allocation, and aggregation transmission tasks within a decentralized partially observable Markov decision process (Dec-POMDP) framework. Furthermore, we introduce an episodic-phase-recalling reward shaping (EPRS) method to correlate the influences across these phases. Inspired by multi-task learning, we propose an efficient multi-agent reinforcement learning (MARL) algorithm featuring a multi-head actor-critic (MH-AC) network structure and task-equalized adaptation (TEA) technology, designed to optimize latency, energy consumption, network traffic, and battery aging. Extensive experiments validate the effectiveness of the proposed method, showing a 29.9% reduction in total training time, an 11.5% reduction in network traffic, and superior overall performance compared to rule-based methods.

The wireless networked control system (WNCS) is a closed-loop hierarchical network that enables interaction among wireless communication, computation, and control components to support various services ranging from information exchange to intelligent decision making. Different from a single communication system aiming for reliable or efficient delivery, WNCS is goal-oriented, highlighting the ultimate control performance requirement guaranteed under various limits of communication, computation, and control resources. In this article, we present a comprehensive survey of WNCS from the communication perspective. We discuss appropriate WNCS architecture, topics, such as sensing strategy design, under energy and bandwidth constraints, state estimation problems in the presence of imperfect channels, and control approaches for WNCS performance. We further pay attention to the joint design within WNCS to achieve well-performing WNCS improvements. Moreover, considering the fact that timely transmission of the measurements is of significance to precise control, we provide a review of WNCS design involving the Age of Information (AoI) that copes with the goal-oriented requirements. The challenges and new research directions are discussed at the end of this survey.