James Beall
How are you?
I just read 2 articles by you about Self-Regulating and Integrating. These articles are very nice and interesting.
Today I test a Classical PID loops using Self-Regulating method according to your articles, the result was satisfied. In addition, I try to use "Ziegler and Nichols" method today, but result was not satisfied.
I used a Simulate Blocks generated a first order plus dead time (FOPDT) dynamic model as my Classical PID PV Value and calculate the required PID tuning constants according to your method in our office.
Now I still have some confusions about Self-Regulating, I am not sure if you can draw some time to give me a help. .
1. How to accurately identify the Times Constants(Tau), I mean If I give a 10% Step out to PID controller, If PID Output from 0 to 10%, the time required for the process to reach 98 percent of its final value maybe take 120 seconds, then I step out PID output from 10% to 20%, the time Constants(Tau) maybe difference, How to identify this time Constants(Tau)? test some times then average all these test values? I ever met a actual case, I give a step output to PID output from 0% to 10%, but no any feedback from Valve, but when I step out PID Output from 10% to 20%, the PV feedback very fast. so to this case, how to calculate time Constant(Tau)?
2. KP=Δ%PV/Δ%OUT, How to calculate the Δ%PV? In my test, I assuming the Δ%PV=1% PV final Value when I give Step output to PID output, but in your article, there is a chart display Δ%PV=2.8%, I have a little confusion, the PV has reached 98% PV final value, the left PV should not exceed 2%, why the Δ%PV=2.8%?
3. When using the integrating tune method? In Your article, you mentioned "Level processes typically have an integrating response" , So I want to know if Level tuning need to use Integrating, the other types loops suit for using self-Regulating?
4. I used Simulate using reached 0.632 final PV value take times as time constants, why using a 0.632 ? I am a little curious for this parameter.
Sorry to ask so many questions, I will appreciate to get your help.
Jimmy
Good questions Jimmy! I am glad you enjoyed the articles! I assume these were the articles that I wrote for the ISA Intech magazine, found at the following links.
Keep in mind that "all process models are wrong, but some are useful"! What I mean by this is that that the process model is never a perfect fit to the model that we choose to approximate it with, we choose a model that is a "good enough" fit to the process response. Secondly, the process model, which really includes all the components in the loop such as the valve, transmitter, I/O filters, etc., varies over the range of operation (even at a single operating point!). It is common to see +-20% variation in the model parameters over a small operating range. If these process model varies too much, linearization techniques are beneficial. So, you don't have to be super accurate on the model identification because it will vary on its own and you must allow for this in the robustness of your tuning.
So, I'll answer your questions below!
1. How to accurately identify the Times Constants(Tau), I mean If I give a 10% Step out to PID controller, If PID Output from 0 to 10%, the time required for the process to reach 98 percent of its final value maybe take 120 seconds, then I step out PID output from 10% to 20%, the time Constants(Tau) maybe difference, How to identify this time Constants(Tau)? test some times then average all these test values? I ever met a actual case, I give a step output to PID output from 0% to 10%, but no any feedback from Valve, but when I step out PID Output from 10% to 20%, the PV feedback very fast. so to this case, how to calculate time Constant(Tau)? James: First, be careful about stepping the out from 0% or 100%, the final control element (FCE), such as a valve, may not be responding from these limits. Depending on the response of the FCE coming off a limit, it could impact the dead time, time constant and process gain. Once the FCE is off the limit, the time constant should be consistent for other step sizes and positions (though as noted above, it does vary!). You must have a data recording system that is fast enough to identify the response dynamics, say 1/3 of Tau or dead time, whichever is smaller.
2. KP=Δ%PV/Δ%OUT, How to calculate the Δ%PV? In my test, I assuming the Δ%PV=1% PV final Value when I give Step output to PID output, but in your article, there is a chart display Δ%PV=2.8%, I have a little confusion, the PV has reached 98% PV final value, the left PV should not exceed 2%, why the Δ%PV=2.8%?James: The figure was trying to show the final change was 2.8% and the 98% value of change would be 0.98 (2.8%)= 2.74%. So, the time, after the dead time, that it takes the PV to change by 2.74% is the T98. And the Tau is T98/4.
3. When using the integrating tune method? In Your article, you mentioned "Level processes typically have an integrating response" , So I want to know if Level tuning need to use Integrating, the other types loops suit for using self-Regulating? James: There are exceptions but typically flow, some temperatures, some pressures, compositions are self-regulating. Even some levels, like a tank with no discharge pump (like a bathtub) are self-regulating.
4. I used Simulate using reached 0.632 final PV value take times as time constants, why using a 0.632 ? I am a little curious for this parameter. James: This is because a first order response, without deadtime, has a response equation where ΔPV=ΔOUT*Kp*(1-e**(-t/tau)). So, at "one time constant" when t=tau, then (1-e**(-t/tau)) = (1-e**(-1)) = (1-0.368) = 0.632, or 63.2%. And, when time equal "4 time constants, (1-e**(-t/tau)) = (1-e**(-4)) = (1-0.00183) = 0.982, or 98%.
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