事件触发机制下网络化系统的模型预测控制研究综述OA
A Review of Research on Model Predictive Control for Networked Control Systems with Event-Triggered Mechanisms
网络化系统的设计集成了控制系统、通信和实时计算等领域的相关技术方案.由于物理环境影响,网络控制系统中的通信带宽及计算资源均受限制,事件触发控制策略能够有效解决通信受限问题.采用事件触发策略后,在改变系统模型的同时影响了系统性能,采用模型预测控制方法能够提升系统的鲁棒性和安全性.文中综述了网络化控制系统中事件触发方案的建模及其与控制器协同设计方法的研究进展,分析介绍了动态事件触发、类切换事件触发、环形事件触发和随机事件触发等触发方案的数学模型,总结了网络化系统中事件触发机制联合设计控制器在实际中的应用,提出了网络化控制系统中基于事件触发的模型预测控制研究存在的问题和未来的研究方向.
The design of networked systems integrates relevant technical solutions from fields such as control sys-tems,communication,and real-time computing.Due to the influence of the physical environment,both the communi-cation bandwidth and computing resources in networked control systems are limited.The event-triggered control strategy can effectively address the issue of communication limitations.After adopting the event-triggered strategy,while changing the system model,it also affects the system performance.The model predictive control method can en-hance the robustness and security of the system.This study reviews the research progress on the modeling of event-trig-gered schemes in networked control systems and the co-design methods with controllers.It analyzes and introduces the mathematical models of triggering schemes such as dynamic event-triggered,switching-like event-triggered,ring e-vent-triggered,and stochastic event-triggered.It summarizes the practical applications of jointly designing controllers with event-triggered mechanisms in networked systems.Moreover,it proposes the existing problems and future research directions of model predictive control based on event-triggered in networked control systems.
黎黄菊;王建华;杜树新
湖州师范学院工学院,浙江湖州 313000湖州师范学院工学院,浙江湖州 313000||湖州市工业系统智能感知与优化控制重点实验室,浙江湖州 313000湖州师范学院工学院,浙江湖州 313000||湖州市工业系统智能感知与优化控制重点实验室,浙江湖州 313000
信息技术与安全科学
网络化控制系统网络安全事件触发采样与通信动态事件触发随机事件触发环形事件触发不确定性模型预测控制
networked control systemsnetwork securityevent-triggered sampling and communicationdynamic event triggeringstochastic event triggeringtorus event triggeringuncertaintymodel predictive control
《电子科技》 2026 (1)
32-39,8
浙江省自然科学基金(LQ22F030011)湖州市自然科学基金(2022YZ35)湖州市工业系统智能感知与优化控制重点实验室项目(2022-17)Natural Science Foundation of Zhejiang(LQ22F030011)Natural Science Foundation of Huzhou(2022YZ35)Project of Huzhou Key Labora-tory of Intelligent Sensing and Optimal Control of Industrial Systems(2022-17)
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