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Recently, robust funnel Model Predictive Control (MPC) was introduced, whichconsists of model-based funnel MPC and model-free funnel control for itsrobustification w.r.t. model-plant mismatches, bounded disturbances, anduncertainties. It achieves output-reference tracking within prescribed boundson the tracking error for a class of unknown nonlinear systems. We extendrobust funnel MPC by a learning component to adapt the underlying model to thesystem data and hence to improve the contribution of MPC. Since robust funnelMPC is inherently robust and the evolution of the tracking error in theprescribed performance funnel is guaranteed, the additional learning componentis able to perform the learning task online - even without an initial model oroffline training.

Safe continual learning in MPC with prescribed bounds on the tracking error