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@article{Bradford:2020:10.1016/j.compchemeng.2020.106844,
author = {Bradford, E and Imsland, L and Zhang, D and del, Rio Chanona EA},
doi = {10.1016/j.compchemeng.2020.106844},
journal = {Computers and Chemical Engineering},
title = {Stochastic data-driven model predictive control using gaussian processes},
url = {http://dx.doi.org/10.1016/j.compchemeng.2020.106844},
volume = {139},
year = {2020}
}

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TY  - JOUR
AB  - Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear control systems with constraints. Gaussian processes (GPs) present a powerful tool to identify the required plant model and quantify the residual uncertainty of the plant-model mismatch. It is crucial to consider this uncertainty, since it may lead to worse control performance and constraint violations. In this paper we propose a new method to design a GP-based NMPC algorithm for finite horizon control problems. The method generates Monte Carlo samples of the GP offline for constraint tightening using back-offs. The tightened constraints then guarantee the satisfaction of chance constraints online. Advantages of our proposed approach over existing methods include fast online evaluation, consideration of closed-loop behaviour, and the possibility to alleviate conservativeness by considering both online learning and state dependency of the uncertainty. The algorithm is verified on a challenging semi-batch bioprocess case study.
AU  - Bradford,E
AU  - Imsland,L
AU  - Zhang,D
AU  - del,Rio Chanona EA
DO  - 10.1016/j.compchemeng.2020.106844
PY  - 2020///
SN  - 0098-1354
TI  - Stochastic data-driven model predictive control using gaussian processes
T2  - Computers and Chemical Engineering
UR  - http://dx.doi.org/10.1016/j.compchemeng.2020.106844
VL  - 139
ER  -

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