The tensor-product (TP) model transformation is a recently proposed numerical method capable of transforming linear parameter varying state-space models to the higher order singular value decomposition (HOSVD) based canonical form of polytopic models. It is also capable of generating various types of convex TP models, a type of polytop models, for linear matrix inequality based controller design. The crucial point of the TP model transformation is that its computational load exponentially explodes with the dimen-sionality of the parameter vector of the parameter-varying state-space model. In this paper we propose a modified TP model transformation that leads to considerable reduction of the computation. The key idea of the method is that instead of transforming the whole system matrix at once in the whole parameter space, we decompose the problem and perform the transformation element wise and restrict the computation to the subspace where the given element of the model varies. The modified TP model transformation can readily be executed in higher dimensional cases when the original TP model transformation fails. The effectiveness of the new method is illustrated with numerical examples.
- Higher order singular value decomposition
- Linear matrix inequality
- Linear parameter varying models
- Polytopic models
- TP model transformalion
ASJC Scopus subject areas
- Control and Systems Engineering