内模强化学习型模型预测控制及其在人工胰脏上的应用(英文)

Internal model control-enhanced learning-type model predictive control: application to artificial pancreas

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.

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 β-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.