Working with lower-order models can simplify analysis and control design. Simpler models are
also easier to understand and manipulate than high-order models. High-order models
obtained by linearizing complex Simulink® models, interconnecting model elements, or other sources can contain
states that do not contribute much to the dynamics of particular interest to your
application. Use the Model Reducer app, the Reduce Model
Order task in the Live Editor, or functions such as
minreal to reduce model
order while preserving model characteristics that are important for your
For more information about ways to reduce model order, see Model Reduction Basics.
|Model Reducer||Reduce complexity of linear time-invariant (LTI) models|
|Reduce Model Order||Reduce complexity of linear time-invariant (LTI) models in the Live Editor|
|Model order reduction|
|Create option set for model order reduction|
|Gramian-based input/output balancing of state-space realizations|
|(Not recommended) Hankel singular values of dynamic system|
|Plot Hankel singular values and return plot handle|
|(Not recommended) Create option set for computing Hankel singular values and input/output balancing|
Model-order reduction can simplify analysis and control design by providing simpler models that are easier to understand and manipulate.
Interactively reduce model order while preserving important dynamics.
Interactively perform model reduction and generate code in a live script using the Reduce Model Order task.
Compute lower-order approximations of higher-order models by removing states with lower energy contributions.
Reduce model order by canceling pole-zero pairs or eliminating states that have no effect on the overall model response.
Reduce model order by eliminating poles that fall outside a specific frequency range.
Examine and compare time-domain and frequency-domain responses of the original and reduced models.