@inproceedings{8d81392410be4a4fb810c4da77b18af8,
title = "Stochastic Model Predictive Control for Linear Systems with Bounded Additive Uncertainties",
abstract = "This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to truncated normal distributed additive uncertainties. The new methods are developed by transforming the chance constraints into deterministic constraints via re-building the robust tube-based model predictive control (RTMPC) framework with flexible initialization. Utilizing the one-step-ahead constraint, the ahead-step tube-based stochastic model predictive control (ATSMPC) algorithm is designed by applying the constantly tightened constraints in all prediction horizons. To further enhance the reliability, the cumulative-step tube-based stochastic model predictive control (CTSMPC) algorithm is developed by computing the tightened constraints based on the propagation of uncertainties along the prediction horizons. The effectiveness of the proposed methods are demonstrated via simulations.",
keywords = "chance constraints, constrained control, model predictive control, stochastic MPC, tube MPC",
author = "Fei Li and Huiping Li and Yuyao He",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Chinese Automation Congress, CAC 2020 ; Conference date: 06-11-2020 Through 08-11-2020",
year = "2020",
month = nov,
day = "6",
doi = "10.1109/CAC51589.2020.9327755",
language = "英语",
series = "Proceedings - 2020 Chinese Automation Congress, CAC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6499--6504",
booktitle = "Proceedings - 2020 Chinese Automation Congress, CAC 2020",
}