Data Sharing and Compression for Cooperative Networked Control
Data Sharing and Compression for Cooperative Networked Control
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发表时间：
2021-09
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通讯作者：
Jiangnan Cheng;M. Pavone;S. Katti;Sandeep P. Chinchali;A. Tang
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作者：
Jiangnan Cheng;M. Pavone;S. Katti;Sandeep P. Chinchali;A. Tang
共享网络时间序列数据（例如蜂窝或电力负载模式）的预测可以改进从流量调度到发电的独立控制应用。通常，预测是在不了解下游控制器的任务目标的情况下设计的，因此只是针对平均预测误差进行优化。然而，这种与任务无关的表示通常太大而无法通过通信网络进行流式传输，并且不强调协作控制的显着时间特征。本文提出了一种学习简洁、高度压缩的预测的解决方案，该预测与模块化控制器的任务目标共同设计。我们对真实蜂窝、物联网 (IoT) 和电力负载数据的模拟表明，我们可以将模型预测控制器的性能提高至少 25\%$，同时传输的数据比竞争方法少 80\%$。此外，我们提出了经典线性二次调节器（LQR）控制问题的网络变体的理论压缩结果。
Sharing forecasts of network timeseries data, such as cellular or electricity load patterns, can improve independent control applications ranging from traffic scheduling to power generation. Typically, forecasts are designed without knowledge of a downstream controller's task objective, and thus simply optimize for mean prediction error. However, such task-agnostic representations are often too large to stream over a communication network and do not emphasize salient temporal features for cooperative control. This paper presents a solution to learn succinct, highly-compressed forecasts that are co-designed with a modular controller's task objective. Our simulations with real cellular, Internet-of-Things (IoT), and electricity load data show we can improve a model predictive controller's performance by at least $25\%$ while transmitting $80\%$ less data than the competing method. Further, we present theoretical compression results for a networked variant of the classical linear quadratic regulator (LQR) control problem.