We exploit an adaptive control technique, namely funnel control, in order to establish both initial and recursive feasibility in Model Predictive Control (MPC) for output-constrained nonlinear systems. Moreover, we show that the resulting feedback controller outperforms the funnel controller both w.r.t. the required sampling rate for a zero-order-hold implementation and required control action. We further propose a combination of funnel control and MPC, exploiting the performance guarantees of the model-free funnel controller during a learning phase and the advantages of the model-based MPC scheme thereafter.
@article{arxiv.1912.01843,
title = {Learning-based Funnel-MPC for output-constrained nonlinear systems},
author = {Thomas Berger and Carolin Kästner and Karl Worthmann},
journal= {arXiv preprint arXiv:1912.01843},
year = {2019}
}