引用本文:陈硕,郑年年,栾小丽,刘飞.基于动态参考规划的鲁棒Tube模型预测跟踪控制[J].控制理论与应用,2022,39(9):1725~1732.[点击复制]
CHEN Shuo,ZHENG Nian-nian,LUAN Xiao-li,LIU Fei.Dynamic reference programming-based robust Tube model predictive tracking control[J].Control Theory and Technology,2022,39(9):1725~1732.[点击复制]
基于动态参考规划的鲁棒Tube模型预测跟踪控制
Dynamic reference programming-based robust Tube model predictive tracking control
摘要点击 1301  全文点击 407  投稿时间：2021-08-22  修订日期：2022-07-20
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DOI编号  10.7641/CTA.2022.10770
  2022,39(9):1725-1732
中文关键词  鲁棒Tube模型预测控制  动态参考规划  设定点跟踪  扰动不变集  最优鲁棒跟踪  控制与优化
英文关键词  robust Tube model predictive control  dynamic reference programming  setpoint tracking  disturbance invariant set  optimal robust tracking  control and optimization
基金项目  国家自然科学基金项目（61991402, 61833007）资助
作者单位E-mail
陈硕 江南大学 物联网工程学院 6201905001@stu.jiangnan.edu.cn 
郑年年 江南大学 物联网工程学院  
栾小丽* 江南大学 物联网工程学院 xiaoli_luan@126.com 
刘飞 江南大学 物联网工程学院  
中文摘要
      为实现扰动和约束作用下对系统的最优鲁棒跟踪, 提出一种动态参考规划(DRP)方法, 设计鲁棒Tube模型预测控制器(RTMPC)将系统状态驱动到以最优跟踪点为中心的扰动不变集内. 基于DRP的RTMPC控制方法, 以多步参考为决策变量, 确保在线优化递归可行性的同时, 增加在线优化的自由度; 另外, 通过设定目标函数惩罚标称状态轨迹和参考稳态之间、以及最后一步参考稳态和设定点之间的加权欧式距离, 可实现最优鲁棒跟踪.
英文摘要
      In order to achieve the goal of optimal robust tracking of the system under perturbations and constraints, a dynamic reference programming (DRP) method is proposed to accomplish the target. Therefore, a method called robust Tube model predictive control (RTMPC) is designed. The RTMPC method is proposed to drive the system state to achieve a disturbance invariant set successfully, which is centered on the optimal tracking point. The RTMPC control method based on DRP takes multi-step reference as the decision variable, which is to ensure the feasibility of online optimization recursion and increase the freedom of online optimization at the same time. In addition, the optimal robust tracking can be achieved by setting an objective function, which is used to punish the weighted Euclidean distance between the nominal state trajectory and the reference steady state, as well as the last step between the reference steady state and the set point.