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arXiv 2409.12469eess.SYcs.SY
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从数据到控制:面向大规模互联网络的形式化组合框架

From Data to Control: A Formal Compositional Framework for Large-Scale Interconnected Networks

Omid Akbarzadeh, Amy Nejati, Abolfazl Lavaei

AI总结:

针对大规模模型未知互联网络的安全控制问题,提出基于含噪声数据的组合式方法,利用控制存储证书组合推导控制障碍证书,将计算复杂度从多项式降为线性,实现全去中心化安全控制并提供正确性保证。

摘要:

我们提出了一种含噪声数据的组合式数据驱动方法,用于设计适用于大规模互联网络的全去中心化安全控制器,这类网络包含大量数学模型未知的子系统。我们的组合方案利用互联拓扑结构,将网络分析拆解为对各独立子系统的研究。同时,我们采用控制存储证书(Control Storage Certificates, CSCs)的概念来刻画子系统间的联合耗散型性质。这些CSC可用于组合推导专门适用于该互联网络的控制障碍证书(Control Barrier Certificate, CBC),从而保障网络的安全性。\n在我们的数据驱动方案中,我们仅在指定时间范围内从每个未知子系统采集一条受噪声干扰的输入-状态轨迹。通过满足特定的秩条件,该过程可为每个子系统构建一个CSC。随后,依据组合耗散性推理,我们将从含噪声数据中得到的CSC进行组合,为未知网络构建CBC,在提供正确性保证的同时,保障其在无限时间范围内的安全性。\n我们证明,在互联网络的含噪声数据场景下,我们的组合式数据驱动方法显著优化了CBC及其鲁棒安全控制器的设计。这一进展通过将计算复杂度从使用平方和(Sum-of-Squares, SOS)优化时随网络维度的多项式增长,降低为随子系统数量的线性增长实现。我们将该数据驱动研究成果应用于多种基准测试,涉及模型未知、互联拓扑多样的物理网络。

英文摘要:

We introduce a compositional data-driven methodology with noisy data for designing fully-decentralized safety controllers applicable to large-scale interconnected networks, encompassing a vast number of subsystems with unknown mathematical models. Our compositional scheme leverages the interconnection topology and breaks down the network analysis into the examination of distinct subsystems. This is accompanied by utilizing a concept of control storage certificates (CSCs) to capture joint dissipativity-type properties among subsystems. These CSCs are instrumental in a compositional derivation of a control barrier certificate (CBC) specialized for the interconnected network, thereby ensuring its safety. In our data-driven scheme, we gather only a single noise-corrupted input-state trajectory from each unknown subsystem within a specified time frame. By fulfilling a specific rank condition, this process facilitates the construction of a CSC for each subsystem. Following this, by adhering to compositional dissipativity reasoning, we compose CSCs derived from noisy data and build a CBC for the unknown network, ensuring its safety over an infinite time horizon, while providing correctness guarantees. We demonstrate that our compositional data-driven approach significantly enhances the design of a CBC and its robust safety controller under noisy data across the interconnected network. This advancement is achieved by reducing the computational complexity from a polynomial growth in relation to network dimension, when using sum-of-squares (SOS) optimization, to a linear scale based on the number of subsystems. We apply our data-driven findings to a variety of benchmarks, involving physical networks with unknown models and diverse interconnection topologies.

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