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5: Calibration and Dynamic Response

  • Page ID
    121975
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    The collected experimental results should connect in some manner to physical principles for which some theoretical background is known. In some cases, the best fit to a known functional form can be applied. Depending on the associated level of confidence (i.e., uncertainty), the emphasis on some data can be elevated via a weighted least-squares approach. Otherwise, mechanical systems can often respond similarly to ordinary differential equations for which constant coefficients indicate the type of response to step and periodic inputs. Subsections are applied to the experimental procedures to calibrate transfer functions or predict types of outcomes from step and periodic inputs.

    Learning Objectives
    • Line of best fit - create functional estimate \(\hat{y}(x)\) of dependent response
      • Typically sources cover only uniform least-squares regression.
      • These are approaches used in the polyfit function from Matlab or Add Trendline tools in Excel.
      • Sometimes data is more or less important so weighting is required (i.e., touchdowns carry more points than field goals; penalty kicks are the same as standard goals in soccer).
    • Dynamic response of signal - review of Diff Eq. lite with specific input–output conditions
      • Forms of the analytical solutions will be provided.
      • Task will be sorting inputs to determine where variables should be inserted.
      • Decision tree of order of ODE and input type will define path to solution.
      • The engineering then comes in the interpretation of the solution.


    5: Calibration and Dynamic Response is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by LibreTexts.

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