Robust Predictive PI Controller Based on First-Order Dead Time Model

Abstract Predictive control algorithms compute the manipulated variable minimizing a cost function considering expected future errors. PI control algorithms can be equipped with predictive properties. Simple predictive control algorithms are derived using approximation of an aperiodic process by a first-order model with dead time. Applying a noise model the robustness properties of the algorithm are enhanced considering plant-model mismatch. The noise filter is considered as a design parameter. Simulation examples demonstrate the behavior of the predictive PI algorithm and the robustifying effect of the noise filter.