Problem
Research questions and friction points this paper is trying to address.
Distributed Model Predictive Control
Communication Demands
Wireless Communication Technologies
Communication Reduction via Semantic-Based Encoding in DMPC Using LSTMs
🤖 AI Summary
为减少DMPC中通信需求，采用基于LSTM的编码-解码网络压缩信息，通过移动机器人测试验证了该方法在降低通信量的同时保持了良好的性能。
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Semantic-Based Encoding
LSTMs
DMPC
Communication Reduction
Distributed Optimization
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