This paper develops a data-driven modeling strategy and predictive controller for the superheater steam temperature (SST) system in the power plant. First, a data-driven Takagi—Sugeno (T-S) fuzzy model is developed to approximate the behavior of the SST control system using the subspace identification (SID) method. Then, a fuzzy model predictive controller is designed based on the TS-fuzzy model to regulate the SST under the input constraints. Simulation results on a 600MW power plant demonstrate the feasibility and effectiveness of the proposed approach.
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