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Reduce Model Order

Reduce complexity of linear time-invariant (LTI) models in the Live Editor

Description

The Reduce Model Order task lets you interactively compute reduced-order approximations of high-order models while preserving model characteristics that are important to your application. The task automatically generates MATLAB® code for your live script. For more information about Live Editor tasks generally, see Add Interactive Tasks to a Live Script.

Working with lower order models can simplify analysis and control design. Simpler models are also easier to understand and manipulate. You can reduce a plant model to focus on relevant dynamics before designing a controller for the plant. You can also use model reduction to simplify a full-order controller. For more information about model reduction and when it is useful, see Model Reduction Basics.

To get started, select a model to reduce and a model-reduction method. For each method, the task gives you controls and plots that help you ensure that your reduced model preserves dynamics that are important for your application.

  • Balanced Truncation — Compute a lower order approximation of your model by removing states with relatively small energy contributions.

  • Mode Selection — Select modes by specifying a frequency range of interest.

  • Pole-Zero Simplification — Eliminate canceling or near-canceling pole-zero pairs.

Related Functions

The model-reduction code that Reduce Model Order generates uses the following functions.

Reduce Model Order task in Live Editor

Open the Task

To add the Reduce Model Order task to a live script in the MATLAB Editor:

  • On the Live Editor tab, select Task > Reduce Model Order.

  • In a code block in your script, type a relevant keyword, such as reduce, balred, or minreal. Select Reduce Model Order from the suggested command completions.

Examples

Related Examples

Parameters

Choose the model to reduce. The list of available models includes proper tf, ss, or zpk models in the MATLAB workspace. The model can be SISO or MIMO, and continuous or discrete.

  • Continuous-time models cannot have time delays. To reduce a continuous-time model with time delays, first use pade to approximate the time delays as model dynamics.

  • Discrete-time models can have time delays. For the Balanced Truncation reduction method, the task uses absorbDelay to convert the delay into poles at z = 0 before reducing the model.

Note

Reduce Model Order assumes that the model time unit (specified in the TimeUnit property of the model) is seconds. For the Balanced Truncation and Mode Selection methods, if your model does not have TimeUnit = 'seconds', use chgTimeUnit to convert the model to seconds.

For each method, the Reduce Model Order task gives you controls and plots that help you ensure that your reduced model preserves dynamics that are important for your application.

  • Balanced Truncation — Compute a lower order approximation of your model by removing states with relatively small energy contributions. To use this method, specify the number of states (order) in the reduced model. The Hankel singular-value plot visualizes the relative energy contribution of each state in the original model. The task discards states with lower energy than the state you select in this plot. This method generates code that uses the balred command.

    For discrete-time model that has time delays, Reduce Model Order uses absorbDelay to convert the delay into poles at z = 0 before reducing the model by balanced truncation. The additional states are reflected in the response plot and Hankel singular-value plot.

  • Mode Selection — Select modes by specifying a frequency range of interest. The task discards dynamics that fall outside the region you specify on the frequency-response plot. This method generates code that uses the freqsep command.

  • Pole-Zero Simplification — Eliminate canceling or near-canceling pole-zero pairs. The task discards pole-zero pairs that cancel with the threshold specified by the Tolerance parameter. Increase the tolerance to discard more states. This method generates code that uses the minreal command.

Balanced Truncation Parameters

Specify the number of states in the reduced-order model. You can use any value that falls between the number of unstable states in the model and the number of states in the original model. For more information, see Balanced Truncation Model Reduction.

Match the DC gain of the reduced model to that of the original model. Select Preserve DC Gain when the DC behavior of the model is important in your application. Clear the parameter to get better matching of higher frequency behavior. For more information, see Balanced Truncation Model Reduction.

By default, Reduce Model Order analyzes Hankel singular values across all frequencies. Restricting this analysis to a particular frequency range is useful when you know the model has modes outside the region of interest to your particular application. When you apply a frequency limit, Reduce Model Order determines which states are the low-energy states to truncate based on their energy contribution within the specified frequency range only.

To limit the analysis of state contributions to a particular frequency range, check Frequency range. Then, drag the vertical cursors on the response plot to specify the frequency range of interest. Alternatively, enter the minimum and maximum frequencies in the text boxes. The unit is rad/s. If your model does not have TimeUnit = 'seconds', use chgTimeUnit to convert the model to seconds.

Reduce Model Order shows you a comparison of the frequency responses between the original and reduced models. You can use this plot to monitor the match between the original and reduced-order models while you experiment with model-reduction parameter values. Available comparison plots are:

  • Model response — Frequency response of the original and reduced models, shown as a Bode plot for SISO models and a singular-value plot for MIMO models.

  • Absolute error plot — Singular values of G-Gr, where G is the original model and Gr is the current reduced model. (For SISO models, the singular-value plot is the magnitude of the frequency response.)

  • Relative error plot — Singular values of (G-Gr)/G. This plot is useful when the model has very high or very low gain in the region that is important to your application. In such regions, absolute error can be misleading.

Mode Selection Parameters

Specify the lower and upper bounds of the frequency range in which to preserve dynamics. You can also use the vertical cursors on the response plot to specify the range. Reduce Model Order discards dynamics outside the specified range.

For more information about this method, see Mode-Selection Model Reduction.

Reduce Model Order shows you a comparison of the frequency responses between the original and reduced models. You can use this plot to monitor the match between the original and reduced-order models while you experiment with model-reduction parameter values. Available comparison plots are:

  • Model response — Frequency response of the original and reduced models, shown as a Bode plot for SISO models and a singular-value plot for MIMO models.

  • Absolute error plot — Singular values of G-Gr, where G is the original model and Gr is the current reduced model. (For SISO models, the singular-value plot is the magnitude of the frequency response.)

  • Relative error plot — Singular values of (G-Gr)/G. This plot is useful when the model has very high or very low gain in the region that is important to your application. In such regions, absolute error can be misleading.

Pole-Zero Simplification Parameters

Specify the margin for pole-zero cancellation. Pole-zero pairs that cancel within this tolerance are removed from the reduced model. You can use the slider to change the tolerance and observe the results in a response plot.

Results Parameters

Reduce Model Order generates code that shows the response of the original and reduced systems on the plot type you specify. Available plots include:

  • Step response

  • Impulse response

  • Bode plot

  • Singular value (sigma) plot

  • Pole-zero plot

Introduced in R2019b