A Control-Oriented qLPV Modeling Framework

Abstract

This paper proposes a framework for selecting affinely parametrized quasi Linear Parameter Varying (qLPV) model structures that facilitates solutions to specific control design tasks encountered in vehicle dynamics applications. Moreover it facilitates the selection of the scheduling variables and provides a framework to decide whether the controller performance can be improved by introducing some estimated parameters as scheduling variables, i.e., if some adaptive strategy is needed or not. The proposed scheme is an iterative process: in every step a suitable model transformation is applied to generate a finite element convex polytopic representation in order to obtain a qLPV model. Then the LMI feasibility of a robust control objective is verified, which is closely related to the original control task. This step provides a selection criterion that sorts out the suitable models from a finite set of model candidates generated by the iterative method.