标  题：Inherent Robustness Properties of Quasi-infinite Horizon MPC

主讲人：于树友 博士

时  间：6月11日 下午13:30

地  点：广知C楼511会议室

报告摘要：The main idea behind model predictive control is to solve an optimization problem online. On one hand, in reality, model/plant mismatches, exogenous disturbances, numerical errors and state measurement errors are present. On the other hand, the MPC control law provides a feedback control only at specific sampling instant and the system is controlled open-loop during adjacent sampling instants. Therefore, robust analysis and synthesis of MPC are of significant theoretical and practical importance. In this talk, we are interested in the analysis of the inherent robustness properties of nominal MPC of nonlinear systems with input constraints, where the disturbances are persistent but bounded and the optimization problem has a terminal constraint. It is worth noting that the analysis does not assume the continuity of the optimal cost functional or of the control law, and hence the results are both more general and of greater practicality than previous ones. It is shown that the degree of robustness depends on the terminal set and the terminal penalty function, the prediction horizon, the upper bound on the disturbances and the logarithmic norm of the considered system.

于树友博士简介：于树友博士现任吉林大学控制科学与工程系副教授。2007－2011年在德国University of Stuttgart攻读博士学位，师从Automatica前主编Frank Allgöwer教授，并于2012年进入吉林大学任教，主要从事模型预测控制的稳定性和鲁棒性研究。于树友博士曾担任第16届国际机器人与自动化大会分会场主席，担任IEEE Trans. Autom. & Contr.、Automatica、自动化学报等多个国内外重要期刊的审稿人。以第一作者身份发表国际SCI源期刊论文十余篇，参与完成多项国家级重点项目。