Understanding Sampling Period 'T' in Digital Control Systems: Tips and Tricks

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Discussion Overview

The discussion revolves around determining the sampling period 'T' in digital control systems, particularly through the use of inverse Laplace transforms and Z transforms. Participants explore various methods and considerations for calculating the sampling period, including the implications of frequency components in continuous signals.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • One participant inquires about the method to derive the sampling period 'T' using inverse Laplace or Z transforms.
  • Another participant presents a result from the inverse Laplace transform and questions whether the frequency of the wave can be determined solely from the sine term or if the exponential term must also be considered.
  • A participant references the Nyquist theorem, suggesting its relevance to determining the minimum sampling frequency based on the continuous signal's frequency.
  • One participant proposes that the minimum sampling frequency is twice the frequency of the continuous signal, leading to a maximum time period that is the inverse of this frequency.
  • Another participant adds that both transient and steady-state frequencies should be considered when determining the sampling rate, suggesting the use of Bode plots to identify these frequencies.
  • A later reply questions whether the function in the frequency domain is a transfer function or a response to an input, indicating that this distinction may affect the analysis.
  • It is noted that the sampling rate derived from the Bode plot will be an approximation, acknowledging the theoretical limitations of sampling in practice.

Areas of Agreement / Disagreement

Participants express differing views on how to approach the calculation of the sampling period 'T', with no consensus reached on the best method or the implications of various frequency components.

Contextual Notes

There are unresolved assumptions regarding the nature of the function being analyzed (transfer function vs. response) and the treatment of transient versus steady-state signals in the context of sampling period determination.

rizwanibn
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< Mentor Note -- thread moved to HH from the technical engineering forums, so no HH Template is shown >[/color]

Hi.
Please see question no. 1 in the attachment.
If i take the inverse Laplace transform or the Z transform, how can i actually get the value of sampling period 'T'.
or is there any other way to solve this?
Thanks...
 

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Ok i took the inverse Laplace transform.
I got

20sin(1.3t) /2.7e^(t/2)
Is the frequency of the wave (2pi f)=1.3 ?

Or do i have to consider the exponential term as well?
 
Do you know what the nyquist theorem is?
 
Well what i am trying to do is,
Finding out the frequency of the continuous signal.
Two times this frequency is the minimum sampling frequency,and inverse of that is the maximum time period.
Please correct me if i am wrong.
Thank you.
 
Almost. You need to take into account the frequency of your transient, as well as your steady state signal. . The initial function you are given is your function in the frequency domain. If you get a bode plot, or look at the poles, you should get an idea of what the frequencies will be.
 
rizwanibn said:
< Mentor Note -- thread moved to HH from the technical engineering forums, so no HH Template is shown >

Hi.
Please see question no. 1 in the attachment.
If i take the inverse Laplace transform or the Z transform, how can i actually get the value of sampling period 'T'.
or is there any other way to solve this?
Thanks...
Is F(s) a transfer function or a response to a delta (impulse) or step input? I'm guessing it's the former even though f(t) looks like a response. But if it's a response you'd have to know what the input is, remove it from F(s), then proceed as below. (You could assume a delta function input of course, in which case the response and transfer functions would be the same.)

In either case there is no transient to consider. You have a low-pass filter with either two real poles or one complex-conjugate pair (hint: which is it?). Draw the Bode frequency plot, then use the Nyquist theorem to come up with the min. sampling rate by picking the rolloff frequency off the plot. (NOTE: that rate will be an approximation. Theoretically, any finite frequency is passed by the network to some extent so an infinitely high sampling rate would be required for 100% accuracy in restoring to the time domain, but you have to cut off at some point in reality.)
 

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