Abstract
AbstractModel predictive control (MPC) has shown to be an efficient technique for real-time flood control. The evaluation of the control performance is, however, typically restricted to a limited s...
Highlights
IntroductionThe number of river floods has steadily increased in many parts of the world (EM-DAT 2005; MEA 2005; Brouwers et al 2015)
During recent decades, the number of river floods has steadily increased in many parts of the world (EM-DAT 2005; MEA 2005; Brouwers et al 2015)
Intelligent control systems are typically tested for single flood events because most classic Model predictive control (MPC) controllers are too computationally demanding to perform long-term analyses
Summary
The number of river floods has steadily increased in many parts of the world (EM-DAT 2005; MEA 2005; Brouwers et al 2015). Model predictive control (MPC) strongly reduces the impact of these economically costly natural disasters in comparison with classic programmable logic controller (PLC)-based control strategies (Barjas-Blanco et al 2010; Breckpot et al 2013; Chiang and Willems 2015). Vermuyten et al (2018a) presented a combination of MPC and a reduced genetic algorithm (RGA) as a successful and fast alternative for classic MPC controllers, utilizing fast conceptual river models. This heuristic optimization method can solve nonlinear, nonconvex optimization problems and has been successfully applied to the Demer basin in Belgium, but only after assuming ideal circumstances of no uncertainties in the river model and the rainfall forecasts. For the single flood events in that case, damage cost reductions between 2% and 31% were obtained with the combined RGA-MPC technique in comparison to the current PLC-based regulation
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