引用本文:苏旭,邹媛媛,牛玉刚,贾廷纲.事件触发双模分布式预测控制[J].控制理论与应用,2016,33(9):1139~1146.[点击复制]
SU Xu,ZOU Yuan-yuan,NIU Yu-gang,JIA Ting-gang.Event-triggered dual-mode distributed model predictive control[J].Control Theory and Technology,2016,33(9):1139~1146.[点击复制]
事件触发双模分布式预测控制
Event-triggered dual-mode distributed model predictive control
摘要点击 4100  全文点击 1909  投稿时间：2015-12-06  修订日期：2016-09-19
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DOI编号  10.7641/CTA.2016.50964
  2016,33(9):1139-1146
中文关键词  大系统  分布式控制系统  模型预测控制  事件触发控制
英文关键词  large-scale systems  distributed control systems  model predictive control  event-triggered control
基金项目  国家自然科学基金项目(61673174, 61374107), 国家科技支撑计划(2015BAF10B00)资助.
作者单位邮编
苏旭 华东理工大学 200237
邹媛媛* 华东理工大学 200237
牛玉刚 华东理工大学 
贾廷纲 上海电气集团股份有限公司自动化事业部 
中文摘要
      本文针对有界扰动作用下的线性离散大系统, 提出了事件触发双模分布式预测控制设计方法. 利用输入状 态稳定性(input-to-state stability, ISS)理论建立了仅与子系统自身信息相关的事件触发条件. 只有子系统满足相应的 事件触发条件, 才进行状态信息的传输和分布式预测控制优化问题的求解, 并与邻域子系统交互最优解作用下的关 联信息. 当子系统进入不变集时, 采用状态反馈控制律进行镇定, 并与进入不变集的邻域子系统不再交互信息. 分析 了算法的递推可行性和系统的闭环稳定性, 给出了扰动的上界. 最后, 通过车辆控制系统对算法进行仿真验证, 结 果表明, 本文提出的方法能够有效降低优化问题的求解次数和关联信息的交互次数, 节约计算资源和通信资源.
英文摘要
      This paper proposes an event-triggered dual-mode distributed model predictive control method for large-scale linear discrete-time systems subject to bounded disturbances. The event-triggering condition, which involves information of the subsystem itself, is obtained by the input-to-state stability (ISS) theory. Only when the event-triggering condition is satisfied, the state measurement is sent and the distributed model predictive control optimization problem is solved. Meanwhile, the subsystem exchanges its optimal state trajectories with neighbor subsystems. When the subsystem enters the invariant set, the state feedback control law will be applied. Moreover, no information will be exchanged between the subsystem and its neighbor subsystems which also enter the invariant sets. The upper bound of disturbances are derived by analyzing the recursive feasibility and closed-loop stability. Finally, the algorithm is verified through the vehicle control systems. Simulation results show that the presented method is able to reduce the solving frequency of optimization problems and the number of information transmissions, thus saving the computation resources and communication resources.