Output-Feedback RLS-Based Model Predictive Control

Tam W. Nguyen; Syed Aseem Ul Islam; Adam L. Bruce; Ankit Goel; Dennis S. Bernstein; Ilya V. Kolmanovsky

doi:10.23919/ACC45564.2020.9148011

Output-Feedback RLS-Based Model Predictive Control

Tam W. Nguyen, Syed Aseem Ul Islam, Adam L. Bruce, Ankit Goel, Dennis S. Bernstein, Ilya V. Kolmanovsky

Electric and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

12 Scopus citations

Abstract

This paper presents recursive-least-squares-based model predictive control (RLSMPC). RLSMPC uses only output feedback, and thus does not require full-state measurements. Online learning is performed through concurrent system identification, and thus no a priori model is needed. RLSMPC employs separate RLS algorithms for identification, offset determination, and control. Variable-rate forgetting is used to facilitate system identification and offset estimation.

Original language	English
Title of host publication	2020 American Control Conference, ACC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2395-2400
Number of pages	6
ISBN (Electronic)	9781538682661
DOIs	https://doi.org/10.23919/ACC45564.2020.9148011
State	Published - 2020/07
Event	2020 American Control Conference, ACC 2020 - Denver, United States Duration: 2020/07/01 → 2020/07/03

Publication series

Name	Proceedings of the American Control Conference
Volume	2020-July
ISSN (Print)	0743-1619

Conference

Conference	2020 American Control Conference, ACC 2020
Country/Territory	United States
City	Denver
Period	2020/07/01 → 2020/07/03

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.23919/ACC45564.2020.9148011

Cite this

Nguyen, T. W., Ul Islam, S. A., Bruce, A. L., Goel, A., Bernstein, D. S., & Kolmanovsky, I. V. (2020). Output-Feedback RLS-Based Model Predictive Control. In 2020 American Control Conference, ACC 2020 (pp. 2395-2400). Article 9148011 (Proceedings of the American Control Conference; Vol. 2020-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC45564.2020.9148011

@inproceedings{c39cb489ea2442c28d3463690174bc86,

title = "Output-Feedback RLS-Based Model Predictive Control",

abstract = "This paper presents recursive-least-squares-based model predictive control (RLSMPC). RLSMPC uses only output feedback, and thus does not require full-state measurements. Online learning is performed through concurrent system identification, and thus no a priori model is needed. RLSMPC employs separate RLS algorithms for identification, offset determination, and control. Variable-rate forgetting is used to facilitate system identification and offset estimation.",

author = "Nguyen, {Tam W.} and {Ul Islam}, {Syed Aseem} and Bruce, {Adam L.} and Ankit Goel and Bernstein, {Dennis S.} and Kolmanovsky, {Ilya V.}",

note = "Publisher Copyright: {\textcopyright} 2020 AACC.; 2020 American Control Conference, ACC 2020 ; Conference date: 01-07-2020 Through 03-07-2020",

year = "2020",

month = jul,

doi = "10.23919/ACC45564.2020.9148011",

language = "英語",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2395--2400",

booktitle = "2020 American Control Conference, ACC 2020",

}

Nguyen, TW, Ul Islam, SA, Bruce, AL, Goel, A, Bernstein, DS & Kolmanovsky, IV 2020, Output-Feedback RLS-Based Model Predictive Control. in 2020 American Control Conference, ACC 2020., 9148011, Proceedings of the American Control Conference, vol. 2020-July, Institute of Electrical and Electronics Engineers Inc., pp. 2395-2400, 2020 American Control Conference, ACC 2020, Denver, United States, 2020/07/01. https://doi.org/10.23919/ACC45564.2020.9148011

Output-Feedback RLS-Based Model Predictive Control. / Nguyen, Tam W.; Ul Islam, Syed Aseem; Bruce, Adam L. et al.
2020 American Control Conference, ACC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 2395-2400 9148011 (Proceedings of the American Control Conference; Vol. 2020-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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T1 - Output-Feedback RLS-Based Model Predictive Control

AU - Nguyen, Tam W.

AU - Ul Islam, Syed Aseem

AU - Bruce, Adam L.

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AU - Bernstein, Dennis S.

AU - Kolmanovsky, Ilya V.

PY - 2020/7

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AB - This paper presents recursive-least-squares-based model predictive control (RLSMPC). RLSMPC uses only output feedback, and thus does not require full-state measurements. Online learning is performed through concurrent system identification, and thus no a priori model is needed. RLSMPC employs separate RLS algorithms for identification, offset determination, and control. Variable-rate forgetting is used to facilitate system identification and offset estimation.

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 American Control Conference, ACC 2020

Y2 - 1 July 2020 through 3 July 2020

ER -

Output-Feedback RLS-Based Model Predictive Control

Abstract

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Conference

ASJC Scopus subject areas

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