On the Instant Iterative Learning MPC for Nonlinear Systems

被引：0

作者：

Sato, Kaito ^{[1
]}

Sawada, Kenji ^{[2
]}

Inoue, Masaki ^{[3
]}

机构：

[1] Univ Elect Communicat, Dept Mech & Intelligent Syst Eng, Tokyo, Japan

[2] Univ Elect Communicat, Info Powered Energy Syst Res Ctr, Tokyo, Japan

[3] Keio Univ, Dept Appl Phys & PhysicoInformat, Yokohama, Kanagawa 2238521, Japan

来源：

2020 59TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) | 2020年

关键词：

Model predictive control; Iterative learning control; continuous optimization;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Model predictive control (MPC) is one of the methods which optimizes the trajectory of the system with the constraints from predicted states of the system. A number of researches have studied its applications, for example, online optimization methods and fast solvers for nonlinear systems, because of its effectiveness. We propose one of the methods to apply online MPC to nonlinear systems based on instant MPC (iMPC). We recast iterative learning MPC (ILMPC) for nonlinear systems as iMPC via the primal-dual gradient algorithm, which we name "i-ILMPC". Finally, a numerical simulation is performed to demonstrate its effectiveness.

引用

页码：1166 / 1171

页数：6

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