Low-Pass Filtering a system, Time constant issue

AI Thread Summary
To low-pass filter simulations effectively, it's crucial to set the time constant based on the system's dynamics to avoid losing important data. The time constant can vary depending on the context, such as the maximum rate of change in the data. For a car steering system, it could refer to the time taken to maneuver from one extreme to another or a smaller range like +10% to -10%. Some suggest that instead of using a low-pass filter, one could simply extract significant data and discard transients or apply techniques like cyclic prefixes. Understanding the specific requirements of the system will guide the appropriate filtering approach.
dolle39
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Hi!

I want to low-pass filter some simulations that I made. The indata to the simulation is built up by several modules and hence at the edges transients will occur. I want to filter these out. The indata in sampled at 20 Hz. How should I set the time constant for the filter? I mean I know that I should relate that to the time constant of the system in order to not filter away important data. But what is the time constant of a system really? Consider a car steering system. Is the time constant the time it takes for the system to manouver from max left to max right? Or is it the time it takes to manouver from +10% to -10%? Some guideluines would be appreciated.
 
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I don't understand why do you need a LPF to do that (i mean, you "can't" do that). If you have a digital signal (the indata sampled at 20hz) just take the important data and throw away the transients. Or you can use a cyclic prefix technique or something like that.
Now, if you need to design a LPF to do another thing, the time constant is well defined in first-order systems (it is defined in superior orders too). The definition depends of the context.
 
What is the maximum rate of change of your data?
 
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