用语义共设计降低通信量，提升多目标系统的控制精度
Semantic Communication and Control Co-Design for Multi-Objective Distinct Dynamics
- 基于自编码器与库普曼算子，线性化状态演化以提取语义动态
- 引入信号时序逻辑建模控制规则，实现91.65%通信样本减少
- 适合需高效通信的多系统协同控制场景，如智能交通与机器人集群
本文提出一种基于机器学习的语义动态建模方法，用于具有不同控制规则和动态特性的相关系统。通过在自编码器框架中引入库普曼算子，构建动态语义库普曼（DSK）模型，将系统状态演化线性化于潜在空间，捕捉基础语义动态；同时，借助信号时序逻辑（STL）构建逻辑语义库普曼（LSK）模型，编码系统特定的控制规则。二者构成逻辑库普曼自编码器框架，在仿真中实现通信样本减少91.65%，并显著提升状态预测精度与控制性能。
原文摘要 · Abstract (English)
This letter introduces a machine-learning approach to learning the semantic dynamics of correlated systems with different control rules and dynamics. By leveraging the Koopman operator in an autoencoder (AE) framework, the system's state evolution is linearized in the latent space using a dynamic semantic Koopman (DSK) model, capturing the baseline semantic dynamics. Signal temporal logic (STL) is incorporated through a logical semantic Koopman (LSK) model to encode system-specific control rules. These models form the proposed logical Koopman AE framework that reduces communication costs while improving state prediction accuracy and control performance, showing a 91.65% reduction in communication samples and significant performance gains in simulation.
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