Model Reduction Techniques
Robust Control Toolbox™ software offers several algorithms for model approximation and order reduction. These algorithms let you control the absolute or relative approximation error, and are all based on the Hankel singular values of the system.
Robust control theory quantifies a system uncertainty as either
additive or multiplicative types. These model reduction routines are also categorized into two groups:
additive error and multiplicative error
types. In other words, some model reduction routines produce a reduced-order model
Gred
of the original model G
with a bound on
the error , the peak gain across frequency. Others produce a reduced-order model
with a bound on the relative error .
These theoretical bounds are based on the “tails” of the Hankel singular values of the model, which are given as follows.
Additive error bound:
Here, σi are denoted the ith Hankel singular value of the original system
G
.Multiplicative (relative) error bound:
Here, σi are denoted the ith Hankel singular value of the phase matrix of the model
G
(see thebstmr
reference page).
Commands for Model Reduction
Top-Level Model Reduction Command
Method |
Description |
---|---|
Main interface to model approximation algorithms |
Normalized Coprime Balanced Model Reduction Command
Method |
Description |
---|---|
Normalized coprime balanced truncation |
Additive Error Model Reduction Commands
Multiplicative Error Model Reduction Command
Method |
Description |
---|---|
Balanced stochastic truncation |
Additional Model Reduction Tools