The model predictive control (MPC) strategy yields the optimization of a performance index with respect to some future control sequence, using predictions of the output signal based on a process model, coping with amplitude constraints on inputs, outputs and states. The course presents an overview of the most important predictive control strategies, the theoretical aspects as well as the practical implications, that makes model predictive control so successful in many areas of industry, such as petro-chemical industry and chemical process industry. Hands-on experience is obtained by MATLAB exercises with academic examples and a industrial simulation of MPC on a two-product (binary) distillation column. Contents of the course:
General introduction. Differences in models and model structures, advantages and limitations. Prediction models in state-space setting. Standard predictive control scheme. Relation standard form with GPC, LQPC and other predictive control schemes. Solution of the standard predictive control problem. Stability, robustness, initial and advanced tuning. Predictive control based on Linear Matrix Inequalities. Robust design in predictive control.