Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems

Bo Li; Shuai Lu; Tong Wu; Ruixian Zhang; Zhengrong Ling

doi:10.1109/CAC57257.2022.10055385

Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems

Bo Li^*
, Shuai Lu
, Tong Wu
, Ruixian Zhang
, Zhengrong Ling

^*Corresponding author for this work

School of Astronautics, Harbin Institute of Technology
Harbin University of Science and Technology

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

Abstract

This article is devoted to the problem of the suboptimal model predictive control for hidden Markov jump systems (HMJSs) with piecewise homogeneous transition probability, which is a class of time-varying transition probabilities. The HMJS under consideration can accurately describe the asynchronous phenomena between the observed mode and the actual one. An observed-mode-dependent MPC approach with recursive feasibility and mean-square stability is designed. Then, the corresponding algorithms are designed, of which the offline algorithm has shown the same control performance and superior computational efficiency as the online one. Finally, some numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	Proceedings - 2022 Chinese Automation Congress, CAC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6212-6217
Number of pages	6
ISBN (Electronic)	9781665465335
DOIs	https://doi.org/10.1109/CAC57257.2022.10055385
State	Published - 2022
Externally published	Yes
Event	2022 Chinese Automation Congress, CAC 2022 - Xiamen, China Duration: 25 Nov 2022 → 27 Nov 2022

Publication series

Name	Proceedings - 2022 Chinese Automation Congress, CAC 2022
Volume	2022-January

Conference

Conference	2022 Chinese Automation Congress, CAC 2022
Country/Territory	China
City	Xiamen
Period	25/11/22 → 27/11/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Hidden Markov jump systems
mean-square stability
piecewise homogeneous TPs
suboptimal model predictive control

Access to Document

10.1109/CAC57257.2022.10055385

Cite this

Li, B., Lu, S., Wu, T., Zhang, R., & Ling, Z. (2022). Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems. In Proceedings - 2022 Chinese Automation Congress, CAC 2022 (pp. 6212-6217). (Proceedings - 2022 Chinese Automation Congress, CAC 2022; Vol. 2022-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAC57257.2022.10055385

@inproceedings{9c1e3dc9df524d088aae9caedce36aaf,

title = "Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems",

abstract = "This article is devoted to the problem of the suboptimal model predictive control for hidden Markov jump systems (HMJSs) with piecewise homogeneous transition probability, which is a class of time-varying transition probabilities. The HMJS under consideration can accurately describe the asynchronous phenomena between the observed mode and the actual one. An observed-mode-dependent MPC approach with recursive feasibility and mean-square stability is designed. Then, the corresponding algorithms are designed, of which the offline algorithm has shown the same control performance and superior computational efficiency as the online one. Finally, some numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.",

keywords = "Hidden Markov jump systems, mean-square stability, piecewise homogeneous TPs, suboptimal model predictive control",

author = "Bo Li and Shuai Lu and Tong Wu and Ruixian Zhang and Zhengrong Ling",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 Chinese Automation Congress, CAC 2022 ; Conference date: 25-11-2022 Through 27-11-2022",

year = "2022",

doi = "10.1109/CAC57257.2022.10055385",

language = "英语",

series = "Proceedings - 2022 Chinese Automation Congress, CAC 2022",

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

pages = "6212--6217",

booktitle = "Proceedings - 2022 Chinese Automation Congress, CAC 2022",

address = "美国",

}

Li, B, Lu, S, Wu, T, Zhang, R & Ling, Z 2022, Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems. in Proceedings - 2022 Chinese Automation Congress, CAC 2022. Proceedings - 2022 Chinese Automation Congress, CAC 2022, vol. 2022-January, Institute of Electrical and Electronics Engineers Inc., pp. 6212-6217, 2022 Chinese Automation Congress, CAC 2022, Xiamen, China, 25/11/22. https://doi.org/10.1109/CAC57257.2022.10055385

Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems. / Li, Bo; Lu, Shuai; Wu, Tong et al.
Proceedings - 2022 Chinese Automation Congress, CAC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6212-6217 (Proceedings - 2022 Chinese Automation Congress, CAC 2022; Vol. 2022-January).

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

TY - GEN

T1 - Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems

AU - Li, Bo

AU - Lu, Shuai

AU - Wu, Tong

AU - Zhang, Ruixian

AU - Ling, Zhengrong

PY - 2022

Y1 - 2022

N2 - This article is devoted to the problem of the suboptimal model predictive control for hidden Markov jump systems (HMJSs) with piecewise homogeneous transition probability, which is a class of time-varying transition probabilities. The HMJS under consideration can accurately describe the asynchronous phenomena between the observed mode and the actual one. An observed-mode-dependent MPC approach with recursive feasibility and mean-square stability is designed. Then, the corresponding algorithms are designed, of which the offline algorithm has shown the same control performance and superior computational efficiency as the online one. Finally, some numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

AB - This article is devoted to the problem of the suboptimal model predictive control for hidden Markov jump systems (HMJSs) with piecewise homogeneous transition probability, which is a class of time-varying transition probabilities. The HMJS under consideration can accurately describe the asynchronous phenomena between the observed mode and the actual one. An observed-mode-dependent MPC approach with recursive feasibility and mean-square stability is designed. Then, the corresponding algorithms are designed, of which the offline algorithm has shown the same control performance and superior computational efficiency as the online one. Finally, some numerical examples are presented to demonstrate the effectiveness of the proposed algorithm.

KW - Hidden Markov jump systems

KW - mean-square stability

KW - piecewise homogeneous TPs

KW - suboptimal model predictive control

UR - https://www.scopus.com/pages/publications/85151279686

U2 - 10.1109/CAC57257.2022.10055385

DO - 10.1109/CAC57257.2022.10055385

M3 - 会议稿件

AN - SCOPUS:85151279686

T3 - Proceedings - 2022 Chinese Automation Congress, CAC 2022

SP - 6212

EP - 6217

BT - Proceedings - 2022 Chinese Automation Congress, CAC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 Chinese Automation Congress, CAC 2022

Y2 - 25 November 2022 through 27 November 2022

ER -

Asynchronous Suboptimal Model Predictive Control for Piecewise Homogeneous Markov Jump Systems

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this