2020/12/24 by Pauline Kergus, Kergus, Pauline, Simone Formentin +5
Engineering · #Advanced Control Systems Optimization #FOS: Electrical engineering #Reservoir Engineering and Simulation Methods #Systems and Control (eess.SY) #Water resources management and optimization #electronic engineering #information engineering
paper · doi:10.48550/arxiv.2012.13224
openalex publication_date 2020/12/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The optimal control of a water reservoir systems represents a challenging problem, due to uncertain hydrologic inputs and the need to adapt to changing environment and varying control objectives. In this work, we propose a real-time learning-based control strategy based on a hierarchical predictive control architecture. Two control loops are implemented: the inner loop is aimed to make the overall dynamics similar to an assigned linear through data-driven control design, then the outer economic model-predictive controller compensates for model mismatches, enforces suitable constraints, and boosts the tracking performance. The effectiveness of the proposed approach as compared to traditional dynamic programming strategies is illustrated on an accurate simulator of the Hoa Binh reservoir in Vietnam. Results show that the proposed approach performs better than the one based on stochastic dynamic programming.