Stochastic Model Predictive Control for Water Level Regulation in a Lake
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(Published version)
Date
2024
Authors
Jibran, M.
Weyer, E.
Wu, W.
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Conference paper
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IFAC-PapersOnLine, 2024, vol.58, iss.2, pp.49-54
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Muhammad Jibran, Erik Weyer, Wenyan Wu
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IFAC Workshop on Integrated Assessment Modeling for Environmental Systems (IAMES) (29 May 2024 - 31 May 2024 : Savona, Italy)
Abstract
In this paper, we propose a water level regulation framework for systems with uncertain model parameters and subject to additive disturbances. Bayesian system identification is used to estimate the distributions of the uncertain parameters. To regulate water levels, Stochastic Model Predictive Control (SMPC) is applied, using a combination of an average approach and a chance-constrained approach. We adopt a scenario approach to obtain a tractable approximation of the optimisation problem. Simulations based on real data from the upper part of the Murray River in Australia show the efficacy of the proposed framework.
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Copyright © 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)