Abstract: A stochastic model predictive control scheme is developed within the context of planning water-level references for an automated channel. The references are provided as inputs to decentralized feedback controllers that regulate the capacity for gravity-fed supply of flow at points along the channel. An operational challenge is to comply with constraints on channel-flow and water-level transients in the automatic response to changes in the off-take flow load and water-level references. The proposed approach is to determine the reference inputs using a model of the channel dynamics that includes a forecast of off-take demand. Specifically, a structured probabilistic representation of the forecast is used to formualte a chance-constrained optimal control problem that is solved in a receding horizon fashion. A scenario-based approximation of this difficult optimal control problem is considered. The size of the approximating quadratic program scales with the prediction horizon and the number of water-level regulation points. But importantly, the size does not ultimately depend on the number of scenarios used to construct the approximation. Simulations and experimental results from an operational channel are presented to demonstrate the method.
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