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I am afraid that the final PID closed loop simulation on the web tool cannot be easily adjusted to add limits on the simulated PID.
You can take the identified model parameters from the PIDTuner and use them to create a custom simulation on Python or Matlab.
The same goes for the tuning provided by the tool, it gives some initial tuning with a simple slider for adjustment, if you wish to fix the overshoot, you have to de-tune the gains (slider to the left) until no overshoot is seen, or set your own PID tuning rules in an external Python or Matlab simulation environment.
It is likely that you are passing the wrong values, you need to pass exactly what comes in and what comes out of the PID block. Refer to this guide for more details:
Hi, thank you for your interest. Sadly we do not provide commercial services, but we do have some material that can guide you through a proper usage of the tool:
Hope this helps
Here are a few tips:
It all boils down to having good (noise and perturbation free) step response data.
Your data does not show any delay, the process is responding immediately to an input change.
If you want the tool to detect a dead-time, it must be able to see the dead time.
I.e. if your process had a dead-time, it would have a flat output during the dead-time after a change in the input.
Regarding the gains, I show all 5 because some PID implementations use gains, other implementations use times. Just plug in the values that your PID implementation requires. Wikipedia is always your friend.
Maybe this helps:
Hey, happy to hear it worked for you with ease.
As you can imagine, the algorithm is rather complex, but generally speaking, it consists of:
- a series of both linear and nonlinear system identification techniques to identify the models based on your data.
- skogestad’s IMC rules for PID tuning based on the models (https://folk.ntnu.no/skoge/publications/2003/tuningPID/more/tuningpaper_reno-2001/tuningpaper_06nov01.pdf)
The PID tuning rules are just given as a tuning starting point. The valuable information are actually the models, you can use any tuning rules over the models to achieve whatever you want with your PID.
Hi, have you had a look at the following guide? If so, is it still unclear?
That is correct, the derivative part is:
PID_D = -(D/Ts) * (yk – ykm1);
Just like in here
Hi, the PID algorithm for simulation is similar to this PID implementation:
Glad to hear the PDF solved some of your questions.
Regarding the size of the increments, it should be large enough, such that the output signal can be clearly distinguishable from noise.
In other words, the size of the increments should be large enough such that the output signal displays a clear curve that the pidtuner tool can identify, but not too large so you avoid damaging your hardware.
As a rule of thumb, I always try to split my operating range in 3 to 5 levels.
Hi, thank you for using pidtuner.
Looking at your test data, it seems that the step response test has not been done properly. Please look at the following guidelines to make a proper step response:
Hi, thank you for using pidtuner.
To help us help you better, it would be useful if you share your pidtuner project link with us.
In the meanwhile, take a look at this presentation to check if you have done all the steps correctly:
Thank you for using pidtuner. Looking at your data, it seems that your step test is not done correctly.
First, the input data does not seem correlated to the output data throughout the experiment, see slide 9 (“Common Mistakes …”) of the following presentation:
Second, it seems you are saturating your actuator, since the output looks like it achieves a constant growth speed instead of a natural growth curve. Maybe try a smaller step change.
Hope this information helps,
But the grayed out text precisely says “Use ctrl+V”, have you tried selecting the cell and then hitting ctrl+v ?