  This paper presents the open-source stochastic model predictive control
framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It
provides several uncertainty propagation methods to predict stochastic moments
of the system state and can consider unknown parts of the system dynamics using
Gaussian process regression. These methods are used to reformulate a stochastic
MPC formulation as a deterministic one that is solved with GRAMPC. The
performance of the presented framework is evaluated using examples from a wide
range of technical areas. The experimental evaluation shows that GRAMPC-S can
be used in practice for the control of nonlinear uncertain systems with
sampling times in the millisecond range.
