Optimal Inversion-Based Iterative Learning Control for Overactuated Systems

被引：10

作者：

Yoon, Deokkyun ^{[1
]}

Ge, Xinyi ^{[2
]}

Okwudire, Chinedum E. ^{[1
]}

机构：

[1] Univ Michigan, Dept Mech Engn, Ann Arbor, MI 48109 USA

[2] Delphi Technol, Powertrain Div, Troy, MI 48098 USA

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2020年 / 28卷 / 05期

基金：

美国国家科学基金会;

关键词：

Convergence; Uncertainty; Optimization; Robustness; Frequency-domain analysis; Resource management; MIMO communication; Coarse-fine stage; iterative learning control (ILC); model uncertainty; multiple-input multiple-output (MIMO) system; optimization; overactuation; robustness; system inversion; ROBUST MONOTONIC CONVERGENCE; DESIGN;

D O I：

10.1109/TCST.2019.2917682

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

An optimal inversion-based iterative learning control (Opt-In ILC) approach for overactuated systems is proposed. The Opt-In ILC update law is formulated as a constrained optimization problem using the plant model. Specifically, the ILC update law is designed to minimize control effort subject to a user-specified error convergence rate. To achieve robust monotonic convergence (MC), a frequency-domain optimization framework is adopted to determine the best plant model and robustness filter for Opt-In ILC. Simulations and experiments on an overactuated coarse-fine stage are used to demonstrate optimal control effort allocation and optimal selection of plant model and robustness filter via the proposed Opt-In ILC approach.

引用

页码：1948 / 1955

页数：8

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