Model reduction for LPV systems based on approximate modal decomposition
Provides a new reduction method for LPV systems that avoids interpolation, addressing a key bottleneck in existing techniques.
The paper introduces a model order reduction technique for large-scale linear parameter varying (LPV) systems that decouples dynamics into smaller LPV subsystems via modal decomposition and hierarchical clustering, avoiding interpolation issues. Numerical case studies demonstrate its applicability.
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.