부산대학교 도서관

The tensor-product (TP) model transformation was proposed recently as a numerical and automaticallyexecutable method which is capable of transforming linear parameter varying (LPV) state-space models into thehigher order singular value decomposition (HOSVD) based canonical form of polytopic models. The crucial disadvantageof the TP model transformation is that its computational load explodes with the density of discretization andthe dimensionality of the parameter vector of the parameter-varying state-space model. In this paper we propose anew algorithm that leads to considerable reduction of the computation in the TP model transformation. The mainidea behind the modified algorithm is to minimize the number of discretized points to acquire as much informationas possible. The modified TP model transformation can readily be executed on a regular computer efficiently andconcisely, especially in higher dimensional cases when the original TP model transformation fails. The paper alsopresents numerical examples to show the effectiveness of the new algorithm.