Adaptive multi-stage NMPC using sigma point principles

A novel robust nonlinear model predictive control scheme (NMPC) based on the multi-stage formulation is introduced in this paper. The scenario tree of Multi-stage NMPC is often built by assuming parametric uncertainty and by considering the minimum and maximum values of the parameters. This can augment the uncertainty set and can result in a performance loss, if more information is available, e.g. in a form of a parametric confidence region. We propose to mitigate this problem by tightly over-approximating the uncertainty set using the so-called sigma-points parameterization. In addition, the plant measurements are used to reduce the size of the uncertainty set and the scenario tree is updated online. The advantages of the proposed scheme over the traditional multistage NMPC are demonstrated for a benchmark case study.