Which data set should I trust more?

In summary, the conversation discusses the use of a DC motor setup with a difference op-amp and feedback through a potentiometer. The individual is using MATLAB to approximate parameters and gain for the motor's transfer function. They pose a question about the difference in motor parameter values when using open or closed loop responses and question whether closed loop responses provide more accurate approximations.
  • #1
kostoglotov
234
6

Homework Statement



Control systems subject. Have a DC motor setup on a difference op-amp, then with gain, and the axel moves a wiper on a pot used for feedback.

I understand the concepts reasonably well enough to complete the assignment.

I'm using MATLAB scripts to approximate parameters for the motors transfer function. As well as required gain to achieve certain percentages of overshoot.

The question I would really love an answer to is this: I get very different motor parameter values depending on whether I'm using closed or open loop responses. Obviously, for an open loop I'm using an open loop model, and for a closed loop I'm using a closed loop model...but I was thinking, wouldn't closed loop responses be less sensitive to variations in the motors parameters, wouldn't closed loop response data be give less accurate approximations of the motors transfer function parameters? Or, everything else being equal, is my closed loop response data better for approximating the motors transfer function?

Homework Equations

The Attempt at a Solution

 
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  • #2
I agree with your intuition that closing the loop can mitigate the behavior of the motor. I am not sure that it would make the parameters impossible to determine, but they certainly may be less obvious.
 

1. What is the source of the data sets?

The source of the data sets is important to consider when determining which one to trust more. Look for reputable sources such as government agencies, academic institutions, or well-known research organizations.

2. How was the data collected?

The method of data collection can greatly impact the reliability of the data. Make sure to look for data that was collected using rigorous and reliable methods, such as randomized controlled trials or large-scale surveys.

3. Are there any conflicts of interest?

It is important to consider if the data sets were funded or sponsored by a company or organization that may have a vested interest in the outcome. This could potentially bias the results and make the data less trustworthy.

4. Has the data been peer-reviewed?

Peer review is a process in which experts in the field review and evaluate the data and methodology used in a study. This can help ensure the accuracy and validity of the data. Look for data sets that have been peer-reviewed by reputable experts in the field.

5. How recent is the data?

Data can become outdated quickly, so it is important to consider the timeliness of the data sets. Look for data that is current and relevant to your research question. Additionally, try to find multiple sources of data to compare and validate the findings.

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