Decrease of the Computational Load of TP Model Transformation

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Tensor Product (TP) model transformation method was proposed recently as an automated gateway between a class of non-linear models and linear matrix inequality based control design. The core of the TP model transformation is the higher order singular value decomposition of a large sized tensor, which requires high computational power that is usually out of a regular computer capacity in case of higher dimensionality. This disadvantage restricts the applicability of the TP model transformation method to linear parameter varying state-space models with smaller number of state values. The aim of this paper is to propose a modification of the TP model transformation. The proposed version needs considerable less computational effort. The paper also presents a numerical example that shows considerable less computational load is necessary for typical problems.

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