Inverting nonminimum-phase systems from the perspectives of feedforward and ILC

J.C.D. van Zundert; T.A.E. Oomen

doi:10.1016/j.ifacol.2017.08.2135

Inverting nonminimum-phase systems from the perspectives of feedforward and ILC

J.C.D. van Zundert, T.A.E. Oomen

Control Systems Technology

Research output: Contribution to journal › Conference article › peer-review

6 Citations (Scopus)

96 Downloads (Pure)

Abstract

System inversion is at the basis of many feedforward and learning control algorithms. This paper aims to analyze several of these approaches in view of their subsequent use, showing inappropriate use that is previously overlooked. This leads to new insights and approaches for both feedforward and learning. The methods are compared in various aspects, including finite vs. infinite preview, exact vs. approximate, and quality of inversion in various norms which directly relates to their use. The results are validated on a nonminimum-phase benchmark system.

Original language	English
Pages (from-to)	12101-12106
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	50
Issue number	1
DOIs	https://doi.org/10.1016/j.ifacol.2017.08.2135
Publication status	Published - Jul 2017
Event	20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress) - Toulouse, France Duration: 9 Jul 2017 → 14 Jul 2017 Conference number: 20 https://www.ifac2017.org/

Keywords

Feedforward control
ILC
Model inversion
Nonminimum phase

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10.1016/j.ifacol.2017.08.2135

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title = "Inverting nonminimum-phase systems from the perspectives of feedforward and ILC",

abstract = "System inversion is at the basis of many feedforward and learning control algorithms. This paper aims to analyze several of these approaches in view of their subsequent use, showing inappropriate use that is previously overlooked. This leads to new insights and approaches for both feedforward and learning. The methods are compared in various aspects, including finite vs. infinite preview, exact vs. approximate, and quality of inversion in various norms which directly relates to their use. The results are validated on a nonminimum-phase benchmark system.",

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AU - Oomen, T.A.E.

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PY - 2017/7

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N2 - System inversion is at the basis of many feedforward and learning control algorithms. This paper aims to analyze several of these approaches in view of their subsequent use, showing inappropriate use that is previously overlooked. This leads to new insights and approaches for both feedforward and learning. The methods are compared in various aspects, including finite vs. infinite preview, exact vs. approximate, and quality of inversion in various norms which directly relates to their use. The results are validated on a nonminimum-phase benchmark system.

AB - System inversion is at the basis of many feedforward and learning control algorithms. This paper aims to analyze several of these approaches in view of their subsequent use, showing inappropriate use that is previously overlooked. This leads to new insights and approaches for both feedforward and learning. The methods are compared in various aspects, including finite vs. infinite preview, exact vs. approximate, and quality of inversion in various norms which directly relates to their use. The results are validated on a nonminimum-phase benchmark system.

KW - Feedforward control

KW - ILC

KW - Model inversion

KW - Nonminimum phase

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DO - 10.1016/j.ifacol.2017.08.2135

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VL - 50

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EP - 12106

JO - IFAC-PapersOnLine

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T2 - 20th World Congress of the International Federation of Automatic Control (IFAC 2017 World Congress)

Y2 - 9 July 2017 through 14 July 2017

ER -

Inverting nonminimum-phase systems from the perspectives of feedforward and ILC

Abstract

Keywords

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