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 or functions such as
reduce model order while preserving model characteristics that are
important for your application.
For more information about ways to reduce model order, see Model Reduction Basics.
|Model Reducer||Reduce complexity of linear time-invariant (LTI) models|
||Model order reduction|
||Create option set for model order reduction|
||Gramian-based input/output balancing of state-space realizations|
||Minimal realization or pole-zero cancelation|
||Structural pole/zero cancellations|
||Eliminate states from state-space models|
||Options for slow-fast decomposition|
||Hankel singular values of dynamic system|
||Plot Hankel singular values and return plot handle|
||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.
This example shows how to reduce model order while preserving important dynamics using the Model Reducer app.
Compute lower-order approximations of higher-order models using the balanced truncation reduction method in the Model Reducer app or at the command line.
Pole-zero simplification reduces the order of your model exactly by canceling pole-zero pairs or eliminating states that have no effect on the overall model response.
Model selection eliminates poles that fall outside a specific frequency range of interest.
The plotting tools in the Model Reducer app let you examine and compare time-domain and frequency-domain responses of the original model and the reduced models you create in the app.