Find phase plot between input and output experimental data?

Actually, I want to know the practical system's frequency response (bandwidth).

The input to the system is a sine wave with varying frequencies (frequency sweep)

plotted the magnitude and phase response using FFT, the magnitude plot was okay but the phase was weird

is there any other way to get correct phase plot or to cross verify the phase plot?

[Rik]

@Anil I recovered the removed content from the Google cache (something which anyone can do). Editing away your question is very rude. Someone spent time reading your question, understanding your issue, figuring out the solution, and writing an answer. Now you repay that kindness by ensuring that the next person with a similar question can't benefit from this answer.

@Star Strider Unfortunately I was unable to find a cached version of your comment, nor would the file be included in that cache, so I left your flag in place.

[Rena Berman]

(Answers Dev) Restored edit

Answer 1

[Star Strider]

I am not certain what function created that plot (it might have been the System Identification Toolbox bode function.)

See if the unwrap function will do what you want. It may be necessary to get the outputs of bode, use unwrap on the phase vector, and then re-plot the data using your own subplot call.

[Star Strider]

I am having a difficult time making sense of these data. What code are you using to analyse them?

In the Excel table, the time values do not match. If the output is sampled at the same times as the input, they should match. Also, while the two ‘Input’ values appear the same in the plots, the magnitudes differ by a factor of about 3E+3. The times in the Excel file are also not regularly sampled, and the sampling frequencies are not equal. I understand that there appears to be a delay in the system being measured, hoiwever the sampling times should be the same regardless.

Please resolve these issues, since I cannot work with these data until they are resolved. That they are not may actually be the problem here.

format long
M1 = readmatrix('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1298570/Input.txt')

M1 = 10000×2

1.0e+04 * 0 1.553700000000000 0.000001017600000 1.556800000000000 0.000002032100000 1.559900000000000 0.000003046700000 1.563000000000000 0.000004054400000 1.566100000000000 0.000005067100000 1.569100000000000 0.000006080600000 1.572100000000000 0.000007095300000 1.574900000000000 0.000008106300000 1.577700000000000 0.000009120600000 1.580400000000000

% C1 = readcell('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1298600/Input%20&%20Output.xlsx')

T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1298600/Input%20&%20Output.xlsx','VariableNamingRule','preserve')

T1 = 104459×6 table
    Time (ms)      output (mm0       Var3    Var4    Time (s)       Input (mm)   
    _________    ________________    ____    ____    ________    ________________

        0        3.45833579783243    NaN     NaN            0     4.9988037109375
       70        3.45836061885825    NaN     NaN     0.010176         5.094921875
       71        3.45484844370411    NaN     NaN     0.020321     5.1910400390625
       72        3.46016014323052    NaN     NaN     0.030467      5.287158203125
       73        3.46260501427422    NaN     NaN     0.040544     5.3832763671875
       74         3.4565735049989    NaN     NaN     0.050671     5.4762939453125
       75        3.46016014323052    NaN     NaN     0.060806     5.5693115234375
       76        3.45991193297228    NaN     NaN     0.070953     5.6561279296875
       77        3.45600262140494    NaN     NaN     0.081063    5.74294433593749
       78         3.4500455752071    NaN     NaN     0.091206       5.82666015625
       79        3.45757875654478    NaN     NaN     0.101351      5.907275390625
       80        3.45998639604975    NaN     NaN     0.111499     5.9847900390625
       81        3.45499736985906    NaN     NaN     0.121622     6.0592041015625
       82        3.46120262631514    NaN     NaN     0.131774     6.1274169921875
       83        3.46097923708273    NaN     NaN     0.141848    6.19252929687499
       84        3.45719403064451    NaN     NaN      0.15197     6.2514404296875

T1{:,1} = T1{:,1}*1E-3;                                         % Convert To seconds

Input = rmmissing(T1{:,[5 6]})

Input = 10000×2

0 4.998803710937501 0.010176000000000 5.094921874999997 0.020321000000000 5.191040039062500 0.030467000000000 5.287158203124996 0.040544000000000 5.383276367187499 0.050671000000000 5.476293945312499 0.060806000000000 5.569311523437499 0.070953000000000 5.656127929687500 0.081063000000000 5.742944335937494 0.091206000000000 5.826660156250000

FsInput = mean(1./diff(Input(:,1)))

FsInput = 
  98.755138173019446

FsdInput = std(1./diff(Input(:,1)))

FsdInput = 
   0.306849580474746

FsOutput = mean(1./diff(T1{:,1}))

FsOutput = 
     9.999809903091644e+02

FsdOutput = std(1./diff(T1{:,1}))

FsdOutput = 
   3.753404440191707

figure
plot(M1(:,1), M1(:,2))
grid

figure
subplot(2,1,1)
plot(T1{:,1}, T1{:,2})
grid
title('Output')
subplot(2,1,2)
plot(Input(:,1), Input(:,2))
grid
title('Input')

figure
subplot(2,1,1)
plot(T1{:,1}, T1{:,2})
grid
xlim([0 20])
title('Output')
subplot(2,1,2)
plot(Input(:,1), Input(:,2))
grid
xlim([0 20])
title('Input')

.