Simulink® Control Design™

Choosing a Linearization Algorithm Method

Options for Linearization Algorithm Method

You can choose from the following two linearization methods in the Simulink^® Control Design™ software:

• Block-by-block analytic linearization (the default method)

• Numerical-perturbation linearization

Note   To use numerical-perturbation linearization, you must select an option in the Linearization Options dialog box of the GUI, or if you are using functions, with the linoptions function.

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Advantages of Block-by-Block Analytical Linearization

The default linearization method, block-by-block analytic linearization, linearizes the blocks individually and then combines the results to produce the linearization of the whole system. This method has several advantages:

• It divides the linearization problem into several smaller, easier problems.

• It defines the system being linearized by input and output markers on the signal lines rather than root-level inport and outport blocks.

• It supports open-loop analysis.

• You can control the linearization of each block by using an analytic linearization that is programmed into the block or by selecting a perturbation level for the block.

• You can compute linearized models with exact representations of continuous time delays.

For more information, see Block-by-Block Analytic Linearization.

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Advantages and Disadvantages of Numerical-Perturbation Linearization

Numerical-perturbation linearization linearizes the whole system by numerically perturbing the system's inputs and states around the operating point. The advantage of this method is that it is quick and simple, especially for large or complicated systems. However, there are also several disadvantages with this method:

• It relies on root-level inport and outport blocks to define the system being linearized.

• There is no support for open-loop analysis.

• You have limited control over the perturbation levels for each block.

• It does not use any of the analytic, preprogrammed block linearizations.

• It is sensitive to scaling issues (models with large and small signal values).

For more information, see Numerical-Perturbation Linearization.

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Understanding Analysis in the Simulink® Control Design™ Software

Block-by-Block Analytic Linearization