o:1389 Control theory-based data assimilation for hydraulic models as a decision-support tool for hydropower systems: sequential, multi-metric tuning of the controllers en ABSTRACT-Increasing renewable energy usage puts an extra pressure on decision-making in river hydropower systems. Decision support tools are used for near-future forecasting of the water available. Modeldriven forecasting used for river state estimation often provides bad results due to numerous uncertainties. False inflows and poor initialization are some of the uncertainty sources. To overcome this, standard data assimilation (DA) techniques (e.g., ensemble Kalman filter) are used, which are not always applicable in real systems. This paper presents further insight into the novel, tailor-made model update algorithm based on control theory. According to water-level measurements over the system, the model is controlled and continuously updated using proportional–integrative–derivative (PID) controller(s). Implementation of the PID controllers requires the controllers’ parameters estimation (tuning). This research deals with this task by presenting sequential, multi-metric procedure, applicable for controllers’ initial tuning. The proposed tuning method is tested on the Iron Gate hydropower system in Serbia, showing satisfying results. hydraulic model update, model-driven forecasting, near future forecasting, PID controller, PID controllers tuning 1552101 1464-7141 1552099 10.2166/hydro.2021.078 https://doi.org/10.2166/hydro.2021.078 2023-03-16T08:09:52.159Z 44 no 46 Miloš Milašinović Građevinski fakultet Univerziteta u Beogradu Dušan Prodanović Građevinski fakultet Univerziteta u Beogradu person Budo Zindović Građevinski fakultet Univerziteta u Beogradu person Boban Stojanović Faculty of Science, Department of Mathematics and Informatics, University of Kragujevac person Nikola Milivojević Institut za vodoprivredu "Jaroslav Černi", Beograd person 0000-0001-5330-1219 application/pdf 1013664 https://unilib.phaidrabg.rs/o:1389 no yes 18 70 92000001 71A04 Journal of Hydroinformatics 23 3 500 516 IWA Publishing 2021 First received 4 May 2020; accepted in revised form 14 January 2021; Available online 22 February 2021