Optimizing Control by Robustly Feasible Model Predictive Control and Application to Drinking Water Distribution Systems

The paper consider optimizing Model Predictive Control (MPC) for nonlinear plants with output constraints under uncertainties. Although the MPC technology can handle the constraint in the model by solving constraint model based optimization task, satisfying the plant output constraints still remains a challenge. The paper proposes Robustly Feasible MPC (RFMPC), which achieves feasibility of the outputs in the controlled plant. The RFMPC is applied to control quantity which is illustrated by application to a Drinking Water Distribution Systems (DWDS) example.

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