| 引用本文: | 井冈山,王龙,史大威,陈通文.网络化系统的学习与控制[J].控制理论与应用,2025,42(11):2100~2113.[点击复制] |
| JING Gang-shan,WANG Long,SHI Da-wei,CHEN Tong-wen.Learning and control for networked systems[J].Control Theory & Applications,2025,42(11):2100~2113.[点击复制] |
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| 网络化系统的学习与控制 |
| Learning and control for networked systems |
| 摘要点击 2061 全文点击 243 投稿时间：2025-06-24 修订日期：2025-10-13 |
| 查看全文 查看/发表评论 下载PDF阅读器 HTML |
| DOI编号 10.7641/CTA.2025.50259 |
| 2025,42(11):2100-2113 |
| 中文关键词 网络化系统 强化学习 数据驱动控制 分布式控制 |
| 英文关键词 networked systems reinforcement learning data-driven control distributed control |
| 基金项目 国家自然科学基金项目(62203073,62533002,62036002), 重庆市自然科学基金面上项目(CSTB2022NSCQ–MSX0577)资助. |
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| 中文摘要 |
| 网络化系统是指由多个具有交互和任务执行能力的子系统基于网络结构连接而成的动态系统.由于在不
同应用场景中经常呈现高维、多约束、非凸、非线性等特点,网络化系统的分析与控制问题在本世纪受到了来自不
同领域的广泛关注.为应对动态环境与复杂系统中的不确定性等问题,具有端到端特性的强化学习方法被引入到
网络化系统的控制策略学习中.然而,网络化系统的高维数与结构化特性使强化学习的效率和有效性面临严峻挑
战. 事实上,许多研究发现,网络化系统的性能与其网络结构密切相关,从图的角度切入能够把复杂的控制问题转化
为简单的组合优化问题,进而实现对实际大规模网络的应用.鉴于此,本文从图的视角出发,对基于学习的网络化
系统控制方法做系统性梳理,通过考察线性二次调节与马尔可夫博弈两种模型下的最优控制问题,揭示图结构在网
络化系统控制策略学习中的重要作用,同时也将列举该领域现存的难题并展望未来的发展方向. |
| 英文摘要 |
| Anetworkedsystemrefers to a system composed of multiple subsystems with the capability of interaction and
task execution, connected through a network. Due to the properties of high dimension, multiple constraints, non-convexity,
and nonlinearity in various application scenarios, the analysis and control of networked systems have received widespread
attention from different communities in this century. To address uncertainties in dynamic environments and complex sys
tems, end-to-end methods like reinforcement learning have been introduced to learn control policies of networked systems.
However, the high-dimensional nature of networked systems poses significant challenges to the learning efficiency. In fa
ct, many studies have found that the performance of networked systems is often closely related to the network structures.
By approaching the problem from a graph perspective, complex optimization and control problems can be transformed
into simple combinatorial optimization problems, enabling the scalability of the method to practical large-scale networks.
In light of this, this paper systematically reviews learning-based control methods for networked systems from the graph
perspective. By examining optimal control problems formulated by linear quadratic regulation and Markov games, respec
tively, it highlights the critical role of graph structures in learning control policies for networked systems. Additionally,
some challenges in this field are outlined and a prospective outlook on future directions is provided. |
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