引用本文:杨跃男,王友清.内模强化学习型模型预测控制及其在人工胰脏上的应用[J].控制理论与应用,2012,29(8):1057~1062.[点击复制]
YANG Yue-nan,WANG You-qing.Internal model control-enhanced learning-type model predictive control: application to artificial pancreas[J].Control Theory and Technology,2012,29(8):1057~1062.[点击复制]
内模强化学习型模型预测控制及其在人工胰脏上的应用
Internal model control-enhanced learning-type model predictive control: application to artificial pancreas
摘要点击 3521  全文点击 2011  投稿时间：2012-05-09  修订日期：2012-07-07
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DOI编号  10.7641/j.issn.1000-8152.2012.8.LCTA120480
  2012,29(8):1057-1062
中文关键词  迭代学习控制  模型预测控制  间接型迭代学习控制  内模控制  人工胰脏  1型糖尿病
英文关键词  iterative learning control (ILC)  model predictive control  indirect ILC  internal model control  artificial pancreas  type 1 diabetes mellitus
基金项目  This work was supported by the National Natural Science Foundation of China (No. 61074081), the Doctoral Fund of Ministry of Education of China (No. 20100010120011), and the Beijing Nova Program (No. 2011025).
作者单位E-mail
杨跃男 北京化工大学 信息科学与技术学院  
王友清* 北京化工大学 信息科学与技术学院 wang_youqing@mail.buct.edu.cn 
中文摘要
      在学习型模型预测控制的框架里, 迭代学习控制器被用来更新模型预测控制器的设定点. 在已经发表的研究成果里, 学习型模型预测控制用到的是比例型的学习率, 这种学习率的学习能力有限, 而且怎样设计学习增益仍然是一个开放性问题. 在本文中, 基于内模控制理论提出的PID型的迭代学习控制器被用来更新模型预测控制器的设定点. 为了方便起见, 本文提出的结合算法可称为内模强化学习型模型预测控制. 本文提出的算法应用在1型糖尿病人的人工胰脏闭环控制上. 仿真结果显示, 本算法对比于比例学习型模型预测控制可以达到更好的收敛性能, 而且对非重复干扰有很好的鲁棒性.
英文摘要
      In the framework of a learning-type model predictive control (L-MPC), an iterative learning control (ILC) is used to update the setpoint for model predictive control (MPC). In the reported studies, the L-MPC usually has a P-type ILC, which has limited learning capability and also how to design its learning gain remains an open problem. A PID-type ILC was proposed to design the learning-type setpoint for MPC based on internal model control (IMC) theory. For convenience, the proposed combination is named IMC-enhanced L-MPC. The proposed method was applied to the closed-loop control of an artificial pancreatic ˉ-cell for type 1 diabetes mellitus (T1DM). The simulation results show that the proposed algorithm can produce superior convergence performance compared with the P-type L-MPC, and also it has excellent robustness to non-repetitive disturbances.