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

In summary, if you have a low-pass filter with two real poles, you can use the Nyquist theorem to find the sampling rate. If you have a low-pass filter with one complex-conjugate pair, you can use the Nyquist theorem to find the sampling rate and the Bode frequency plot to find the rolloff frequency.
  • #1
rizwanibn
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< 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...
 

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  • #2
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?
 
  • #3
Do you know what the nyquist theorem is?
 
  • #4
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.
 
  • #5
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.
 
  • #6
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 frequecy 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.)
 

1. What is the sampling period 'T' in digital control systems?

The sampling period 'T' in digital control systems refers to the time interval between two consecutive samples of a continuous signal. It is a crucial parameter that determines the accuracy and stability of the digital control system.

2. Why is understanding the sampling period important in digital control systems?

The sampling period is important because it directly affects the performance of the digital control system. If the sampling period is too long, the system may not be able to accurately capture and control the continuous signal. On the other hand, if the sampling period is too short, it can lead to excessive computational burden and instability in the system.

3. How do I determine the appropriate sampling period for my digital control system?

The appropriate sampling period can be determined by considering the characteristics of the system, such as the bandwidth and frequency response, as well as the required accuracy and stability. It is also important to consider the Nyquist-Shannon sampling theorem, which states that the sampling period should be at least twice the highest frequency present in the signal.

4. Can I change the sampling period during operation?

In most cases, it is not recommended to change the sampling period during operation as it can significantly affect the performance of the digital control system. However, in some cases, it may be necessary to adjust the sampling period to accommodate changes in the signal or system dynamics.

5. Are there any tips or tricks for choosing the sampling period in digital control systems?

Some tips and tricks for choosing the sampling period include using a higher sampling frequency than the minimum required by the Nyquist-Shannon sampling theorem, implementing anti-aliasing filters to reduce noise, and considering the trade-off between accuracy and computational burden. It is also important to carefully analyze the system dynamics and choose a sampling period that can accurately capture the relevant information.

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