To reduce the order of a model, you can either simplify your model, or compute a lowerorder approximation. The following table summarizes the differences among several modelreduction approaches.
Approach  Commands 

Simplification — Reduce model order exactly by canceling polezero pairs or eliminating states that have no effect on the overall model response 

Approximation — compute a lowerorder approximation  balred — Compute a lowerorder approximation
of your model by neglecting states that have relatively low effect
on the overall model response 
In some cases, approximation can yield better results, even
if the model looks like a good candidate for simplification. For example,
models with near polezero cancellations may be better reduced by
approximation than simplification. Similarly, using balred
to
reduce statespace models can yield more accurate results than minreal
.
When you use a reducedorder 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 bode
or sigma
.
Or, compare the openloop responses for the original and reduced plant
and controller models.