  Recently, robust funnel Model Predictive Control (MPC) was introduced, which
consists of model-based funnel MPC and model-free funnel control for its
robustification w.r.t. model-plant mismatches, bounded disturbances, and
uncertainties. It achieves output-reference tracking within prescribed bounds
on the tracking error for a class of unknown nonlinear systems. We extend
robust funnel MPC by a learning component to adapt the underlying model to the
system data and hence to improve the contribution of MPC. Since robust funnel
MPC is inherently robust and the evolution of the tracking error in the
prescribed performance funnel is guaranteed, the additional learning component
is able to perform the learning task online - even without an initial model or
offline training.
