An environment for the efficient testing and implementation of robust NMPC

In the last years many research studies have presented simulation or experimental results using Nonlinear Model Predictive Control (NMPC). The computation times needed for the solution of the resulting nonlinear optimization problems are in many cases no longer an obstacle due to the advances in algorithms and computational power. However, NMPC is not yet an industrial reality as its linear counterpart is. Two reasons for this are the lack of good tool support for the development of NMPC solutions and the fact that it is difficult to ensure the robustness of NMPC to plant-model mismatch. In this paper, we address both these issues. The main contribution is the development of an environment for the efficient implementation and testing of NMPC solutions, offering flexibility to test different algorithms and formulations without the need to re-encode the model or the algorithm. In addition, we present and discuss the approach of multi-stage robust NMPC to systematically deal with the robustness issue. The benefits of our approach are illustrated by experimental results on a laboratory plant.