Model Predictive Control of water resources systems: A review and research agenda
Andrea Castelletti(RFF-CMCC European Institute on Economics and the Environment), J. M. Maestre(Universidad de Sevilla), Andrea Cominola(Politecnico di Milano), Sergio Lucia(TU Dortmund University), Matteo Giuliani(RFF-CMCC European Institute on Economics and the Environment), Andrea Ficchì(University of Reading), Wenyan Wu(The University of Melbourne), Carlos Ocampo‐Martínez(Universitat Politècnica de Catalunya), Pablo Segovia(Delft University of Technology), Bart De Schutter(Delft University of Technology)
Cited by 96
Related Papers
Twenty-three unsolved problems in hydrology (UPH) – a community perspective
|Hydrological Sciences Journal|2019|1.1k
Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
|Journal of Process Control|2013|347
Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning
|IEEE Transactions on Smart Grid|2016|339
Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning
|IEEE Transactions on Cybernetics|2020|290
A comparative analysis of distributed MPC techniques applied to the HD-MPC four-tank benchmark
|Journal of Process Control|2011|203