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    ESC Calibration

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    • Alex KushleyevA
      Alex Kushleyev ModalAI Team @dlee
      last edited by

      @dlee

      First, please make sure that you are running the calibration with propellers installed (and use safety precautions).

      Next, if you are doing several calibration tests (which sometimes makes sense) and want to use an average value, you have to be a little careful. Keep in mind that you cannot just average four a0 values and use it for a0_average (and so on). Also, even though the a0, a1, a2 numbers may look different, it is difficult to just look at the numbers and see how similar or different the curves are - the answer is to plot them.

      I have written a short script to plot your four results using python. You can run it and take a look (first you may need to install numpy and plotly python packages using pip3 install numpy plotly.

      import numpy as np
      import plotly.graph_objects as go
      
      rpms = np.arange(0,20000) #rpm range for the quadratic fit
      
      cals = []
      fits = []
      
      cals.append([7.53669159958e-06, 0.336081465707, 112.878194918])
      cals.append([2.9479165331e-06,  0.37353561309,  35.6265065345])
      cals.append([8.8413818561e-06,  0.3256003514,   132.362831923])
      cals.append([7.1523119742e-07,  0.405821707061, -98.2189291548])
      
      fig = go.Figure()
      
      for idx in range(len(cals)):
          fit = np.polyval(cals[idx], rpms)
          fits.append(fit)
          fig.add_trace(go.Scatter(x=rpms, y=fits[idx], name='Fit %d'%idx))
      
      
      fig.update_layout(title='Motor Voltage vs. RPM')
      fig.update_xaxes(title_text="RPM")
      fig.update_yaxes(title_text="Motor Voltage (mV)")
      fig.show()
      

      The resulting plot looks like below, the four plots are not quite the same. But i have a feeling you might not have used propellers on during calibration? please confirm.

      4c98e125-8fb0-4b0b-9350-d9b7f42e1fc7-image.png

      dleeD 1 Reply Last reply Reply Quote 0
      • Alex KushleyevA Alex Kushleyev referenced this topic on
      • dleeD
        dlee @Alex Kushleyev
        last edited by

        @Alex-Kushleyev
        It seems that when I run the script once, it only calibrates for one motor. So I ran the script 4 times to calibrate 4 motors. The motors spinned when I ran each script. Is there something I'm doing wrong?
        I ran that script inside the drone, so I commented out the code to draw the plot.

        Alex KushleyevA 1 Reply Last reply Reply Quote 0
        • Alex KushleyevA
          Alex Kushleyev ModalAI Team @dlee
          last edited by

          @dlee , the calibration script indeed only spins one motor at a time, by design. Can you please confirm that you calibrated with propellers on?

          dleeD 1 Reply Last reply Reply Quote 0
          • dleeD
            dlee @Alex Kushleyev
            last edited by

            @Alex-Kushleyev Yes, I calibrated with propellers on.

            Alex KushleyevA 1 Reply Last reply Reply Quote 0
            • Alex KushleyevA
              Alex Kushleyev ModalAI Team @dlee
              last edited by

              @dlee

              Got it. Can you tell me what was the maximum rpm reached during the calibration? I think i may have used too high rpm in the plot.

              dleeD 1 Reply Last reply Reply Quote 0
              • dleeD
                dlee @Alex Kushleyev
                last edited by

                @Alex-Kushleyev Maximum RPM was reached at 13,000. I am using 2800kv motors.

                Alex KushleyevA 1 Reply Last reply Reply Quote 0
                • Alex KushleyevA
                  Alex Kushleyev ModalAI Team @dlee
                  last edited by Alex Kushleyev

                  @dlee

                  Thanks. If you look at the plot at 13K rpm, the plots look much closer together. Also please note that there are two curves that are close together and another two that are also close together. I think they may correspond to CW and CCW rotating propellers. Sometimes the CW and CCW propellers are not exactly the same and could result in slightly different calibration..

                  I think for your initial testing you can use either of the calibration curves. However i am wondering whether your propellers are not symmetric CW and CCW.

                  You can also use a calibration that is an average. I will follow up soon how to calculate that.

                  dleeD 2 Replies Last reply Reply Quote 0
                  • dleeD
                    dlee @Alex Kushleyev
                    last edited by

                    @Alex-Kushleyev I checked that all of propellers spin right side (top-left & bottom-right: CCW; top-right & bottom-left : CW).

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                    • dleeD
                      dlee @Alex Kushleyev
                      last edited by

                      @Alex-Kushleyev Is there any update?

                      Alex KushleyevA 1 Reply Last reply Reply Quote 0
                      • Alex KushleyevA
                        Alex Kushleyev ModalAI Team @dlee
                        last edited by

                        @dlee , sorry for the delay.

                        Just to clarify, it is possible that your CW and CCW propellers are not exactly the same, therefore the CW and CCW motors are showing slightly different response to calibration. In this case we can calculate an average for this calibration and use that for all 4 motors. I modified the script to calculate the quadratic fit for all four calibration results together.

                        import numpy as np
                        import plotly.graph_objects as go
                        
                        rpms = np.arange(0,13000) #rpm range for the quadratic fit
                        
                        cals = []
                        fits = []
                        all_fits = []
                        
                        #enter the calibration results from each motor
                        cals.append([7.53669159958e-06, 0.336081465707, 112.878194918])
                        cals.append([2.9479165331e-06,  0.37353561309,  35.6265065345])
                        cals.append([8.8413818561e-06,  0.3256003514,   132.362831923])
                        cals.append([7.1523119742e-07,  0.405821707061, -98.2189291548])
                        
                        fig = go.Figure()
                        
                        for idx in range(len(cals)):
                            fit = np.polyval(cals[idx], rpms)
                            fits.append(fit)
                            fig.add_trace(go.Scatter(x=rpms, y=fits[idx], name='Fit %d'%idx))  #plot each fit
                        
                        #create an array that contains points sampled from each curve
                        #and perform a polynomial fit on all the data to find the average
                        all_data = np.array(fits).flatten('C')
                        all_rpms = np.array([rpms,rpms,rpms,rpms]).flatten('C')
                        
                        #evaluate the average poly fit
                        ply = np.polyfit(all_rpms, all_data, 2)
                        av_fit = np.polyval(ply, rpms)
                        
                        
                        #print the average fit coefficients
                        print('Average Fit coefficients:')
                        print('    pwm_vs_rpm_curve_a0 = ' + str(ply[2]))
                        print('    pwm_vs_rpm_curve_a1 = ' + str(ply[1]))
                        print('    pwm_vs_rpm_curve_a2 = ' + str(ply[0]))
                        
                        #plot the average
                        fig.add_trace(go.Scatter(x=rpms, y=av_fit, name='Average Fit'))
                        
                        #finalize and show the figure
                        fig.update_layout(title='Motor Voltage vs. RPM')
                        fig.update_xaxes(title_text="RPM")
                        fig.update_yaxes(title_text="Motor Voltage (mV)")
                        fig.show()
                        

                        It results in the following plot and average coefficients. You can enter these coefficients into your custom esc parameters xml file.

                        Average Fit coefficients:
                            pwm_vs_rpm_curve_a0 = 45.66215105517507
                            pwm_vs_rpm_curve_a1 = 0.36025978431449995
                            pwm_vs_rpm_curve_a2 = 5.01030529655002e-06
                        

                        2dd75e2f-f722-464d-be82-5548568ec25b-image.png

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