Sequential and Simultaneous Model Predictive Control of a Drainage Canal Network Using an Implicit Diffusive Wave Model
Publication: Journal of Irrigation and Drainage Engineering
Volume 143, Issue 3
Abstract
Model predictive control (MPC) is an efficient approach to regulate water systems, for both water quantity and quality. It generates optimal control trajectories based on model predictions over a finite horizon. In this research, the focus is on nonlinear MPC with a nonlinear internal model and the comparison of a sequential and simultaneous optimization setup, referred to as sequential and simultaneous nonlinear model predictive control (SeNMPC, SiNMPC), for the solution of the optimum control problem. The representation of the water system in the internal model is based on the diffusive wave model. The model is integrated in time by an unconditionally stable Backward Euler scheme to avoid model instabilities and time step restrictions. This numerical robustness is essential in real-time control applications where large control time steps should not be jeopardized by local grid refinements owing to the system topology. In order to speed up the optimization, an adjoint model is set up to calculate analytical derivatives of the objective function with respect to the optimization variables. Both SeNMPC and SiNMPC are successfully tested on a drainage canal network to regulate water levels and lead to identical results. The SiNMPC shows some advantages over the SeNMPC approach in terms of a higher computational performance and easier options to constrain the optimum control problem.
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Published In
Journal of Irrigation and Drainage Engineering
Volume 143 • Issue 3 • March 2017
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Nov 30, 2015
Accepted: Apr 13, 2016
Published online: Jun 29, 2016
Discussion open until: Nov 29, 2016
Published in print: Mar 1, 2017
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