Moment Matching Based Model Reduction for LPV State-Space Models

TL;DR

This paper introduces a new algorithm for reducing the state dimension of LPV state-space models, ensuring the reduced model closely approximates the original's input-output behavior for specific sequences.

Contribution

The paper proposes a novel moment matching algorithm for LPV models that preserves input-output behavior and acts as a reachability and observability reduction.

Findings

Reduces LPV model order while maintaining input-output behavior

Applicable to affine dependence LPV models

Provides a reachability and observability reduction method

Abstract

We present a novel algorithm for reducing the state dimension, i.e. order, of linear parameter varying (LPV) discrete-time state-space (SS) models with affine dependence on the scheduling variable. The input-output behavior of the reduced order model approximates that of the original model. In fact, for input and scheduling sequences of a certain length, the input-output behaviors of the reduced and original model coincide. The proposed method can also be interpreted as a reachability and observability reduction (minimization) procedure for LPV-SS representations with affine dependence.