Learning Model Predictive Control for Iterative Tasks. A Data-Driven Control Framework

被引：250

作者：

Rosolia, Ugo ^{[1
]}

Borrelli, Francesco ^{[1
]}

机构：

[1] Univ Calif Berkeley, Dept Mech Engn, Berkeley, CA 94701 USA

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2018年 / 63卷 / 07期

关键词：

Data driven; iterative learning control; learning; optimal control; predictive control; safety; BATCH PROCESSES; STABILITY; SYSTEMS;

D O I：

10.1109/TAC.2017.2753460

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A learning model predictive controller for iterative tasks is presented. The controller is reference-free and is able to improve its performance by learning from previous iterations. A safe set and a terminal cost function are used in order to guarantee recursive feasibility and nondecreasing performance at each iteration. This paper presents the control design approach, and shows how to recursively construct terminal set and terminal cost from state and input trajectories of previous iterations. Simulation results show the effectiveness of the proposed control logic.

引用

页码：1883 / 1896

页数：14

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