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stepinfo lets you compute step-response characteristics for a dynamic system model or for an array of step-response data. For a step response y(t), stepinfo computes characteristics relative to yinit and ylast, where yinit is the initial offset, that is, the value before the step is applied, and yfinal is the steady-state value of the response. These values depend on the syntax you use.

For an array of step-response data [y,t] , stepinfo uses yinit = 0 and yfinal = last sample value of y , unless you explicitly specify these values.

The following figure illustrates some of the characteristics stepinfo computes for a step response. For this response, assume that y(t) = 0 for t < 0, so yinit = 0.

Given that a loyal companion off Crawl-Kid, Mary Jane has been placed in harm’s way too many moments, concise in which she started initially to just get a hold of herself since the an effective damsel from inside the worry, and now have undesirable to other superheroes

S = stepinfo( sys ) computes the step-response characteristics for a dynamic system model sys . This syntax uses yinit = 0 and yfinal = steady-state value for computing the characteristics that depend on these values.

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S = stepinfo( y , t ) computes step-response characteristics from an array of step-response data y and a corresponding time vector t . For SISO system responses, y is a vector with the same number of entries as t . For MIMO response data, y is an array containing the responses of each I/O channel. This syntax uses yinit = 0 and the last value in y (or the last value in each channel’s corresponding response data) as yfinal.

S = stepinfo( y , t , yfinal ) computes step-response characteristics relative to the steady-state value yfinal . This syntax is useful when you know that the expected steady-state system response differs from the last value in y for reasons such as measurement noise. This syntax uses yinit = 0.