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Model predictive control (MPC) is an efficient control method to regulate water systems, for both water quantity and quality. It can generate optimal control solutions based on model predictions over a finite horizon. There are many ways of categorizing MPC. In this research, the predictive control uses a nonlinear internal model and solves optimization in a sequential manner, thus called Sequential Nonlinear Model Predictive Control (SeNMPC). An implicit scheme is applied on the internal model, diffusive wave model, to avoid time step limitation for model stability. This is often important in real-time control applications where large control time step and fine model girds are used. In order to speed up the computation of optimization, an adjoint method is applied to analytically calculate derivatives of the objective function with respect to control variables. The time reduction is significant. SeNMPC is successfully tested on a drainage canal to regulate water levels.


Session R65, Parameter Estimation: Optimization Applications in Water Resources



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