To reduce the order of a model, you can either simplify your model, or compute a lower-order approximation. The following table summarizes the differences among several model-reduction approaches.
|Simplification — Reduce model order exactly by canceling pole-zero pairs or eliminating states that have no effect on the overall model response|
|Approximation — compute a lower-order approximation|
In some cases, approximation can yield better results, even
if the model looks like a good candidate for simplification. For example,
models with near pole-zero cancellations may be better reduced by
approximation than simplification. Similarly, using
reduce state-space models can yield more accurate results than
When you use a reduced-order model, always verify that the simplification
or approximation preserves model characteristics that are important
for your application. For example, compare the frequency responses
of the original and reduced models using
Or, compare the open-loop responses for the original and reduced plant
and controller models.