{"id":2801,"date":"2013-10-16T08:45:12","date_gmt":"2013-10-16T13:45:12","guid":{"rendered":"https:\/\/blogs.mathworks.com\/seth\/?p=2801"},"modified":"2013-10-16T14:21:25","modified_gmt":"2013-10-16T19:21:25","slug":"sllinearizer-batch-linearization-of-simulink-models","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/simulink\/2013\/10\/16\/sllinearizer-batch-linearization-of-simulink-models\/","title":{"rendered":"slLinearizer: Batch Linearization of Simulink Models"},"content":{"rendered":"

This week I am happy to welcome guest bloggers Charu Fadnavis and Erman Korkut<\/a> to introduce the new slLinearizer<\/tt><\/a> interface from Simulink Control Design<\/a>.<\/p>\r\n\r\n

\"Charu<\/p>\r\n\r\n

linearize vs. slLinearizer<\/strong><\/p>\r\n\r\n

If you have used Simulink Control Design in the past, the first thing you might wonder is: Why a new interface to linearize Simulink models?<\/em><\/p>\r\n\r\n

If you compare the linearize<\/tt> function with the slLinearizer<\/tt> interface, you will realize that many linear analysis tasks can be accomplished by both. The main advantage of the slLinearizer<\/tt> interface is that it makes it easier to obtain multiple transfer functions without recompiling the model, also known as Batch Linearization<\/a>. To get more details on the differences between those two approaches, we recommend going through the documentation page Choosing Batch Linearization Tools<\/a>.<\/p>\r\n\r\n

The slLinearizer Interface<\/strong><\/p>\r\n\r\n

Let's go through a simple example illustrating the basic of the slLinearizer<\/tt> Interface. For that, we will use a very simple model:<\/p>\r\n\r\n

\"simple<\/p>\r\n\r\n

The first step is to create a slLinearizer<\/tt> object for your model.<\/p>\r\n\r\n

\"slLinearizerInterface\"<\/p>\r\n\r\n

Once the object is created, you want to add analysis points. One important difference when compared to the linearize<\/tt> workflow is that with the slLinearizer<\/tt> interface, the points that you specify are much more flexible. When adding points, you do not need to specify if they are input, output, open or closed loop, etc.<\/p>\r\n\r\n

To add points, you can use block name and port number, or signal names. For this example, let's add multiple points.<\/p>\r\n\r\n

\"adding<\/p>\r\n\r\n

We can now use any of the four get*<\/tt> methods to obtain transfer functions for the different points and loop openings: getIOTransfer<\/tt><\/a>, getLoopTransfer<\/tt><\/a>, getSensitivity<\/tt><\/a>, getCompSensitivity<\/tt><\/a>.<\/p>\r\n\r\n

For example, if I want to get the transfer function between the reference r<\/tt> and the output y:<\/tt><\/p>\r\n\r\n

\"I\/O<\/p>\r\n\r\n

I use getIOTransfer<\/tt>:<\/p>\r\n\r\n

\"Result<\/p>\r\n\r\n

If I want the sensitivity at the control signal u<\/tt>:<\/p>\r\n\r\n

\"Sensitivity\"<\/p>\r\n\r\n

I use getSensitivity<\/tt><\/p>\r\n\r\n

\"Sensitivity\"<\/p>\r\n\r\n

The important thing to note, is that the model will be compiled only the first time one of the get*<\/tt> method is called<\/strong>.<\/p>\r\n\r\n

Batch linearization<\/strong><\/p>\r\n\r\n

In addition to linearizing for multiple analysis points and loop opening without recompiling the model, the slLinearizer<\/tt> interface makes it easy to obtain multiple linearizations for a set of operating points or parameter values.<\/p>\r\n\r\n

For the example above, let's say we want to obtain a series of transfer functions for different values of controller gain k<\/tt>, all you have to do is:<\/p>\r\n\r\n

\"Batch<\/p>\r\n\r\n

and you obtain an array of transfer functions:<\/p>\r\n\r\n

\"Step<\/p>\r\n\r\n

If what you need to study is linearization at multiple operating points, it can also be done with the slLinearizer<\/tt> interface. For a detailed example of this workflow, see Vary Operating Points and Obtain Multiple Transfer Functions Using slLinearizer<\/a>.<\/p>\r\n\r\n

Now It's Your Turn<\/strong><\/p>\r\n\r\n

Check out the documentation for slLinearizer and try linearizing your model with it. We'd love to hear your thoughts<\/a>!<\/p>\r\n\r\n\r\n\r\n<\/p>\r\n","protected":false},"excerpt":{"rendered":"

<\/div>

This week I am happy to welcome guest bloggers Charu Fadnavis and Erman Korkut to introduce the new slLinearizer interface from Simulink Control Design.\r\n\r\n\r\n\r\nlinearize vs. slLinearizer\r\n\r\nIf you... read more >><\/a><\/p>","protected":false},"author":41,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[115,65,143,16],"tags":[345,116,344],"_links":{"self":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts\/2801"}],"collection":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/users\/41"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/comments?post=2801"}],"version-history":[{"count":32,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts\/2801\/revisions"}],"predecessor-version":[{"id":2857,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/posts\/2801\/revisions\/2857"}],"wp:attachment":[{"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/media?parent=2801"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/categories?post=2801"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.mathworks.com\/simulink\/wp-json\/wp\/v2\/tags?post=2801"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}