Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

Zhihua Xiong; Yingdong Hong; Chen Chen; Jie Zhang; Maiying Zhong

doi:10.1109/ACC.2016.7525090

Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

Zhihua Xiong^*, Yingdong Hong, Chen Chen, Jie Zhang, Maiying Zhong

^*Corresponding author for this work

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

Abstract

The convergence analysis of two-dimensional based integrated predictive iterative learning control (2D-IPILC) is presented for batch process in presence of output noises. In the 2D-IPILC method, iterative learning control (ILC) in the batch domain is integrated reasonably with real-time model predictive control (MPC) in the time domain. Based on the 2D system theory, system response of output tracking error is described explicitly by using the state-transition matrix of the 2D system for the first time. Then influence of output noise to the tracking error can be revealed clearly. By using this description of model response, convergence properties of the 2D-IPILC can be also analyzed theoretically. The sufficient convergence conditions of output tracking error in the proposed algorithm are derived for a class of linear systems. Simulation results have demonstrated the effectiveness of the proposed method.

Original language	English
Title of host publication	2016 American Control Conference, ACC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1259-1264
Number of pages	6
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7525090
Publication status	Published - 28 Jul 2016
Externally published	Yes
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: 6 Jul 2016 → 8 Jul 2016

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Conference

Conference	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	6/07/16 → 8/07/16

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10.1109/ACC.2016.7525090

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Xiong, Z., Hong, Y., Chen, C., Zhang, J., & Zhong, M. (2016). Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory. In 2016 American Control Conference, ACC 2016 (pp. 1259-1264). Article 7525090 (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7525090

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title = "Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory",

abstract = "The convergence analysis of two-dimensional based integrated predictive iterative learning control (2D-IPILC) is presented for batch process in presence of output noises. In the 2D-IPILC method, iterative learning control (ILC) in the batch domain is integrated reasonably with real-time model predictive control (MPC) in the time domain. Based on the 2D system theory, system response of output tracking error is described explicitly by using the state-transition matrix of the 2D system for the first time. Then influence of output noise to the tracking error can be revealed clearly. By using this description of model response, convergence properties of the 2D-IPILC can be also analyzed theoretically. The sufficient convergence conditions of output tracking error in the proposed algorithm are derived for a class of linear systems. Simulation results have demonstrated the effectiveness of the proposed method.",

author = "Zhihua Xiong and Yingdong Hong and Chen Chen and Jie Zhang and Maiying Zhong",

note = "Publisher Copyright: {\textcopyright} 2016 American Automatic Control Council (AACC).; 2016 American Control Conference, ACC 2016 ; Conference date: 06-07-2016 Through 08-07-2016",

year = "2016",

month = jul,

day = "28",

doi = "10.1109/ACC.2016.7525090",

language = "English",

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Xiong, Z, Hong, Y, Chen, C, Zhang, J & Zhong, M 2016, Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory. in 2016 American Control Conference, ACC 2016., 7525090, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 1259-1264, 2016 American Control Conference, ACC 2016, Boston, United States, 6/07/16. https://doi.org/10.1109/ACC.2016.7525090

Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory. / Xiong, Zhihua; Hong, Yingdong; Chen, Chen et al.
2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 1259-1264 7525090 (Proceedings of the American Control Conference; Vol. 2016-July).

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

TY - GEN

T1 - Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

AU - Xiong, Zhihua

AU - Hong, Yingdong

AU - Chen, Chen

AU - Zhang, Jie

AU - Zhong, Maiying

PY - 2016/7/28

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N2 - The convergence analysis of two-dimensional based integrated predictive iterative learning control (2D-IPILC) is presented for batch process in presence of output noises. In the 2D-IPILC method, iterative learning control (ILC) in the batch domain is integrated reasonably with real-time model predictive control (MPC) in the time domain. Based on the 2D system theory, system response of output tracking error is described explicitly by using the state-transition matrix of the 2D system for the first time. Then influence of output noise to the tracking error can be revealed clearly. By using this description of model response, convergence properties of the 2D-IPILC can be also analyzed theoretically. The sufficient convergence conditions of output tracking error in the proposed algorithm are derived for a class of linear systems. Simulation results have demonstrated the effectiveness of the proposed method.

AB - The convergence analysis of two-dimensional based integrated predictive iterative learning control (2D-IPILC) is presented for batch process in presence of output noises. In the 2D-IPILC method, iterative learning control (ILC) in the batch domain is integrated reasonably with real-time model predictive control (MPC) in the time domain. Based on the 2D system theory, system response of output tracking error is described explicitly by using the state-transition matrix of the 2D system for the first time. Then influence of output noise to the tracking error can be revealed clearly. By using this description of model response, convergence properties of the 2D-IPILC can be also analyzed theoretically. The sufficient convergence conditions of output tracking error in the proposed algorithm are derived for a class of linear systems. Simulation results have demonstrated the effectiveness of the proposed method.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory

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