Robust Model Predictive Control of a Drum Boiler with Online Uncertainty Estimation

In drum boiler control, model predictive control can provide accurate tracking under constraints, but residual model uncertainty still causes safety violations when parameters drift. Purpose. The study developed a robust model predictive controller for a drum boiler in which the safety margin is adjusted online according to the uncertainty level. Methods. А Takagi—Sugeno model with recursive least squares parameter adaptation described the nonlinear plant. Uncertainty was estimated from two sources: the covariance of the identified parameters and a sliding window of prediction errors. The combined estimate tightened output constraints and adjusted control smoothing. The robust layer does not require robust invariant set computation. Results. Numerical experiments on a drum boiler model compared four controller configurations during a 3000-step scenario with three phases of parameter drift. The configuration that combined model adaptation with the robust layer reduced the integral absolute error by 58.3 percent and reduced the number of constraint violations by 47.8 percent relative to the nominal controller. The maximum violation magnitude decreased by 33.8 percent. The configuration with the robust layer alone achieved the smallest number of violations, although it produced less accurate tracking. The average computation time per control step was 2.6 ms. Practical relevance. The proposed algorithm can be integrated into an existing predictive controller as a lightweight safety layer for thermal power equipment. Discussion. The results support the joint use of online identification and adaptive safety margins; future work should address multi-step uncertainty propagation and experimental validation on hardware.

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