Data-Driven Control of Large-Scale Networks With Formal Guarantees: A Small-Gain-Free Approach

• 成果类型: Article
• 署名作者: Samari, Behrad; Nejati, Amy; Lavaei, Abolfazl
• 署名单位: Newcastle University - UK
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2026.3676316
• 发表日期: 2026
• 关键词: dynamical-systems; symbolic models; identification; abstraction; STABILITY

摘要

This article offers a data-driven divide-and-conquer strategy to analyze large-scale interconnected networks, characterized by both unknown mathematical models and interconnection topologies. Our data-driven scheme treats an unknown network as an interconnection of individual agents (a.k.a. subsystems) and aims at constructing their symbolic models, referred to as discrete-domain representations of unknown agents, by collecting data from their trajectories. The primary objective is to synthesize a control strategy that guarantees desired behaviors over an unknown network by employing local controllers, derived from symbolic models of individual agents. To achieve this, we leverage the concept of alternating sub-bisimulation function (ASBF) to establish a relation between the state trajectories of each unknown agent and its data-driven symbolic model. Under a newly developed data-driven compositional condition, we then establish an alternating bisimulation function between an unknown network and its symbolic model based on ASBFs of individual agents while providing correctness guarantees. Despite the sample complexity in monolithic studies being exponential with respect to the network size, we demonstrate that our divide-and-conquer strategy reduces it to the subsystem level. We also showcase that our data-driven compositional condition does not necessitate the traditional small-gain condition, which demands precise knowledge of the interconnection topology for its fulfillment. We apply our data-driven findings to three benchmarks comprising unknown networks with an arbitrary, a priori undefined number of agents and unknown interconnection topologies.