A novel control-oriented multi-affine qLPV modeling framework

This paper proposes a framework for selecting affinely parametrized qLPV model structures that facilitates solutions to specific control design tasks. 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 Tensor Product (TP) model transformation is applied to generate a finite element convex polytopic representation in order to obtain a quasi Linear Parameter Varying model. Then the LMI feasability 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 TP method.