Derivation of Certification-Based Admissibility Dashboard of NMPC Implementation Settings: Framework and Associated Python Package

Abstract

This brief presents a framework that delivers a certification-oriented dashboard of admissible nonlinear model predictive control (NMPC) implementation settings. This differs from the commonly adopted performance-centered tuning approaches by providing a dashboard of admissible setting options for which the optimal choice might be context-dependent. Some of the considered parameters are scarcely tuned in the literature on model predictive control (MPC)-parameter tuning such as the control updating period and the precision of the internal prediction. Moreover, a freely available Python-based implementation is also proposed, and typical results on an illustrative example are discussed highlighting the relevance of the contribution.