Robust Multi-stage Nonlinear Model Predictive Control

Model Predictive Control (MPC) has become one of the most popular control techniques in the process industry mainly because of its ability to deal with multiple-input-multiple-output plants and with constraints. However, its performance can deteriorate in the presence of model uncertainties and disturbances. In the last years, the development of robust MPC techniques has been widely discussed, but these were rarely, if at all, applied in practice due to their conservativeness or their computational complexity. This talk presents multi-stage nonlinear model predictive control (multi-stage NMPC) as a promising non-conservative robust NMPC control scheme, which is applicable in real-time. The approach is based on the representation of the evolution of the uncertainty by a scenario tree. It leads to non-conservative robust control of the plant because it takes into account explicitly that new information (usually present in the form of measurements) will become available at future time steps and that the future control inputs can be adapted accordingly, acting as recourse variables. The approach is illustrated using a challenging industrial case-study, which shows that multi-stage NMPC is a promising strategy for the optimizing control of uncertain nonlinear systems subject to hard constraints. It is also shown that multi-stage NMPC performs better than standard NMPC or other robust NMPC approaches presented in the literature while still being implementable in real-time despite of the main challenge of the method: The size of the resulting optimization problem. Finally, it is illustrated that thanks to the flexibility of the presented approach, it is possible to integrate it with other existing methods to enhance its capabilities and performance.