  The paper presents a novel model order reduction technique for large-scale
linear parameter varying (LPV) systems. The approach is based on decoupling the
original dynamics into smaller dimensional LPV subsystems that can be
independently reduced by parameter varying reduction methods. The decomposition
starts with the construction of a modal transformation that separates the modal
subsystems. Hierarchical clustering is applied then to collect the dynamically
similar modal subsystems into larger groups. The subsystems formed from the
groups are then independently reduced. This approach substantially differs from
most of the previously proposed LPV model reduction techniques, since it
performs manipulations on the LPV model and not on a set of linear
time-invariant (LTI) models defined at fixed scheduling parameter values.
Therefore the model interpolation, which is the most challenging part of most
reduction techniques, is avoided. The applicability of the developed algorithm
is thoroughly investigated and demonstrated by numerical case studies.
