An Extended Kalman Filter for Data-Enabled Predictive Control

被引:26
作者
Alpago, Daniele [1 ]
Dorfler, Florian [2 ]
Lygeros, John [2 ]
机构
[1] Univ Padua, Dept Informat Engn, I-35131 Padua, Italy
[2] Swiss Fed Inst Technol, Dept Informat Technol & Elect Engn, CH-8092 Zurich, Switzerland
来源
IEEE CONTROL SYSTEMS LETTERS | 2020年 / 4卷 / 04期
基金
欧洲研究理事会;
关键词
Optimization; Computational modeling; Trajectory; Linear systems; Predictive control; Stochastic processes; Kalman filters; Optimal control; stochastic systems; Kalman filtering; DESIGN;
D O I
10.1109/LCSYS.2020.2998296
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The literature dealing with data-driven analysis and control problems has significantly grown in the recent years. Most of the recent literature deals with linear time-invariant systems in which the uncertainty (if any) is assumed to be deterministic and bounded; relatively little attention has been devoted to stochastic linear time-invariant systems. As a first step in this direction, we propose to equip the recently introduced Data-enabled Predictive Control algorithm with a data-based Extended Kalman Filter to make use of additional available input-output data for reducing the effect of noise, without increasing the computational load of the optimization procedure.
引用
收藏
页码:994 / 999
页数:6
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