Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty

Yanquan Zhang; Min Cheng; Bin Nan; Shunli Li

doi:10.1109/CCDC58219.2023.10326853

Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty

Yanquan Zhang
, Min Cheng
, Bin Nan
, Shunli Li^*

^*Corresponding author for this work

School of Astronautics, Harbin Institute of Technology

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

Abstract

In this work, we present a tube-based stochastic model predictive control (SMPC) method for nonlinear discrete-time systems subject to unbounded disturbances. The covariance-based probability invariant set (PIS) is formulated by an affine feedback law determined online. The nonlinear chance constraint is first approximated by linear form and then recursively tightened as the optimization algorithm converges. The solution to the original stochastic optimal control problem in tube SMPC is generated by successively solving a series of deterministic semi-definite cone programs. Besides, the recursive feasibility for tube SMPC is established with mild assumption. The efficiency of algorithm is verified by an experiment on six degree of freedom (DOF) spacecraft close proximity under external disturbances.

Original language	English
Title of host publication	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2837-2843
Number of pages	7
ISBN (Electronic)	9798350334722
DOIs	https://doi.org/10.1109/CCDC58219.2023.10326853
State	Published - 2023
Externally published	Yes
Event	35th Chinese Control and Decision Conference, CCDC 2023 - Yichang, China Duration: 20 May 2023 → 22 May 2023

Publication series

Name	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

Conference

Conference	35th Chinese Control and Decision Conference, CCDC 2023
Country/Territory	China
City	Yichang
Period	20/05/23 → 22/05/23

Keywords

probability invariant set
recursive constraint tightening
recursive feasibility
spacecraft close proximity
tube-based SMPC

Access to Document

10.1109/CCDC58219.2023.10326853

Cite this

Zhang, Y., Cheng, M., Nan, B., & Li, S. (2023). Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023 (pp. 2837-2843). (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC58219.2023.10326853

Zhang, Yanquan ; Cheng, Min ; Nan, Bin et al. / Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty. Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2837-2843 (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023).

@inproceedings{33c61b87b1494e5391f70eabfaa05acb,

title = "Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty",

abstract = "In this work, we present a tube-based stochastic model predictive control (SMPC) method for nonlinear discrete-time systems subject to unbounded disturbances. The covariance-based probability invariant set (PIS) is formulated by an affine feedback law determined online. The nonlinear chance constraint is first approximated by linear form and then recursively tightened as the optimization algorithm converges. The solution to the original stochastic optimal control problem in tube SMPC is generated by successively solving a series of deterministic semi-definite cone programs. Besides, the recursive feasibility for tube SMPC is established with mild assumption. The efficiency of algorithm is verified by an experiment on six degree of freedom (DOF) spacecraft close proximity under external disturbances.",

keywords = "probability invariant set, recursive constraint tightening, recursive feasibility, spacecraft close proximity, tube-based SMPC",

author = "Yanquan Zhang and Min Cheng and Bin Nan and Shunli Li",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",

year = "2023",

doi = "10.1109/CCDC58219.2023.10326853",

language = "英语",

series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

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

pages = "2837--2843",

booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

address = "美国",

}

Zhang, Y, Cheng, M, Nan, B & Li, S 2023, Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty. in Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 2837-2843, 35th Chinese Control and Decision Conference, CCDC 2023, Yichang, China, 20/05/23. https://doi.org/10.1109/CCDC58219.2023.10326853

Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty. / Zhang, Yanquan; Cheng, Min; Nan, Bin et al.
Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2837-2843 (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023).

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

TY - GEN

T1 - Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty

AU - Zhang, Yanquan

AU - Cheng, Min

AU - Nan, Bin

AU - Li, Shunli

PY - 2023

Y1 - 2023

N2 - In this work, we present a tube-based stochastic model predictive control (SMPC) method for nonlinear discrete-time systems subject to unbounded disturbances. The covariance-based probability invariant set (PIS) is formulated by an affine feedback law determined online. The nonlinear chance constraint is first approximated by linear form and then recursively tightened as the optimization algorithm converges. The solution to the original stochastic optimal control problem in tube SMPC is generated by successively solving a series of deterministic semi-definite cone programs. Besides, the recursive feasibility for tube SMPC is established with mild assumption. The efficiency of algorithm is verified by an experiment on six degree of freedom (DOF) spacecraft close proximity under external disturbances.

AB - In this work, we present a tube-based stochastic model predictive control (SMPC) method for nonlinear discrete-time systems subject to unbounded disturbances. The covariance-based probability invariant set (PIS) is formulated by an affine feedback law determined online. The nonlinear chance constraint is first approximated by linear form and then recursively tightened as the optimization algorithm converges. The solution to the original stochastic optimal control problem in tube SMPC is generated by successively solving a series of deterministic semi-definite cone programs. Besides, the recursive feasibility for tube SMPC is established with mild assumption. The efficiency of algorithm is verified by an experiment on six degree of freedom (DOF) spacecraft close proximity under external disturbances.

KW - probability invariant set

KW - recursive constraint tightening

KW - recursive feasibility

KW - spacecraft close proximity

KW - tube-based SMPC

UR - https://www.scopus.com/pages/publications/85181837234

U2 - 10.1109/CCDC58219.2023.10326853

DO - 10.1109/CCDC58219.2023.10326853

M3 - 会议稿件

AN - SCOPUS:85181837234

T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

SP - 2837

EP - 2843

BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 35th Chinese Control and Decision Conference, CCDC 2023

Y2 - 20 May 2023 through 22 May 2023

ER -

Zhang Y, Cheng M, Nan B, Li S. Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2837-2843. (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). doi: 10.1109/CCDC58219.2023.10326853

Tube-based stochastic model predictive control for spacecraft close proximity under external uncertainty

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