Data-Driven Robust MPC for an Industrial Evaporator under Model Uncertainty and Sensor Degradation
Model predictive control is attractive for industrial processes because constraints and multivariable interactions can be handled explicitly, but its performance depends on the credibility of the prediction model and the measurements supplied to the controller. This article develops a data-driven robust MPC framework for an industrial evaporator in which model uncertainty and sensor degradation are treated jointly. The open DaISy industrial evaporator benchmark (code 96-010) supplies the data basis: 6305 samples, three manipulated inputs and three measured outputs. Publicly reproducible system-identification results for this benchmark report a first experiment of 3300 samples, an order-eight prediction-error model, and five-step output mean-square errors of 0.0576, 0.1564 and 0.0193. These error scales are used to define relative residual uncertainty for a closed-loop benchmark-constrained surrogate. The controller combines a trust-weighted state estimate with scenario-based robust prediction across a finite uncertainty ensemble. Bias, drift, precision loss and dropout are injected into the sensor channels, and at the same time plant matrices are perturbed independently across Monte Carlo trials. In 200 composite-degradation scenarios with 12% plant-model uncertainty, the proposed robust MPC reduces mean normalized tracking RMSE from 0.2750 for nominal MPC to 0.2421, and reduces normalized performance-envelope exceedance from 14.12% to 6.02%. The improvement is obtained at a 6.75% increase in average control effort. The results show that a practical data-driven robust MPC architecture should not treat identification error and sensor integrity as separate problems; both must enter the online state estimate and the predictive optimization layer.