AccScience Publishing / IJOCTA / Online First / DOI: 10.36922/IJOCTA025400167
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RESEARCH ARTICLE

Tube-based stochastic model predictive control with flexible state initialization

Fei Li1 ,  Chuanfeng Li1* ,  Chao Wu1,2* ,  Shuzhen Wang1* ,  Pengcheng Han1,2 ,  Yan Wang1
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1 School of Power Electrical Engineering, Luoyang Institute of Science and Technology, Luoyang, Henan, China
2 Henan International Joint Laboratory of Testing and Control Technology for Lithium-ion Batteries, Luoyang, Henan, China
IJOCTA, 025400167 https://doi.org/10.36922/IJOCTA025400167
Received: 3 October 2025 | Revised: 19 November 2025 | Accepted: 11 December 2025 | Published online: 8 January 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

The robust model predictive control does not exploit the potentially existing statistical properties of system uncertainties, which may result in overly conservative control solutions. To address this issue, this paper proposes a novel approach of stochastic model predictive control, specifically tailored for linear time-invariant systems that are confronted with bounded additive uncertainties. The proposed method is established within the robust tube-based model predictive control framework, where chance constraints are transformed into deterministic ones. In particular, by leveraging the propagation characteristics of uncertainties, an algorithm of time-varying tube-based stochastic model predictive control is devised through computing tightened constraints along the prediction horizons. Furthermore, utilizing the infinite-horizon propagation property of uncertainties, a constant tube-based stochastic model predictive control method is derived by implementing conservatively constant tightened constraints throughout the entire prediction horizons. The feasibility and closed-loop stability results are rigorously developed, and a numerical example is provided to demonstrate the efficacy of the proposed method.

Graphical abstract
Keywords
Model predictive control
Stochastic model predictive control
Tube model predictive control
Constrained control
Chance constraint
Funding
This research was funded in part by Henan Provincial Funds for Key Research and Development Special Project (Grant no. 251111211800); in part by Henan Provincial Funds for Major Science and Technology Special Project (Grant no. 251100210200); in part by the Natural Science Foundation of Henan Province (Grant no. 252300420380, 252300420253); in part by Henan Provincial Funds for Science and Technology Project (Grant no. 242102311244, 252102211094); in part by Henan Provincial Funds or Higher Education Institutions Key Research Project Plan (Grant no. 24A413005); and in part by Postgraduate Education Reform and Quality Improvement Project of Henan Province (Grant no. YJS2025AL140).
Conflict of interest
The authors declare they have no competing interests.
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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing
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