Model reduction of multidimensional and uncertain systems

Model reduction methods are presented for systems represented by a linear fractional transformation (LFT) on a repeated scalar uncertainty structure, These methods involve a complete generalization of balanced realizations, balanced Gramians, and balanced truncation model reduction with guaranteed error bounds, based on solutions to a pair of linear matrix inequalities (LMI's) which generalize Lyapunov equations. The resulting reduction methods immediately apply to uncertainty simplification and slate order reduction in the case of uncertain systems but also may be interpreted as state order reduction for multidimensional systems.