在分布式模型预测控制（DMPC）中基于语义编码的通信量减少方法
Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
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中文总结 AI 辅助
本研究针对DMPC通信过载问题，采用基于LSTM的编码器-解码器网络减少通信量，在移动机器人编队测试中实现可靠性能，支持不同预测时域长度且无需重训。
中文摘要 AI 辅助
分布式模型预测控制（DMPC）中，智能体每时间步至少需交换大量信息，其通信需求甚至会超出先进无线通信技术的承载能力。为从语义层面减少通信需求，本研究在分布式优化算法中采用基于长短期记忆（LSTM）单元的编码器-解码器网络，智能体发布消息的简化表示，接收方收到后重构原始消息。在移动机器人编队的少通信测试中，经训练的网络保持了令人满意的性能，且在超出全通信承载能力的条件下仍可靠工作。结果表明，使用LSTM可实现前所未有的重构精度，或支持不同预测时域长度而无需重新训练。
英文摘要
The communication demands of distributed model prediction control (DMPC) can overwhelm even advanced wireless communication technologies as agents must exchange a significant amount of information at least once per time step. To semantically reduce communication demands, this work employs encoder-decoder networks built around long-short term memory (LSTM) cells in a distributed optimization algorithm. Agents publish a reduced representation of a message and receivers reconstruct the original message upon reception. In tests with reduced communication using formations of mobile robots, trained networks retain satisfactory performance and work reliably under conditions overwhelming full communication. As the results show, the usage of LSTMs either allows unprecedented reconstruction accuracy or the usage of different prediction-horizon lengths without the necessity to retrain.
发表机构
- LUT University（拉普兰塔理工大学）
机构由 AI 辅助整理，请以论文原文为准。
