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On 01/05/2018 10:43 AM, alessio31183
wrote:
I've designed a new sensor where, unlikely, a temperature change makes a
change in reading. Of course I need to compensate this unwanted behavior.
I did some tests to make this table that is the error in measurement in
[°/°C]
Angle ° 5°C 25°C 50°C
181.10 0.0184 0.0000 -0.0167
219.56 0.0041 0.0000 -0.0010
263.90 -0.0153 0.0000 0.0193
5.45 -0.0151 0.0000 0.0173
48.17 0.0028 0.0000 -0.0042
77.03 0.0141 0.0000 -0.0179
124.98 0.0229 0.0000 -0.0252
165.05 0.0155 0.0000 -0.0275
It's a nice exercise showing Octave's practical usefulness. First,
you want to see the data and display it:
a=sortrows([
181.10 0.0184 0.0000 -0.0167
219.56 0.0041 0.0000 -0.0010
263.90 -0.0153 0.0000 0.0193
5.45 -0.0151 0.0000 0.0173
48.17 0.0028 0.0000 -0.0042
77.03 0.0141 0.0000 -0.0179
124.98 0.0229 0.0000 -0.0252
165.05 0.0155 0.0000 -0.0275
],1)
[ang,temp]=meshgrid(a(:,1),[5,25,50])
mesh(ang,temp,a(:,2:end)')
So, if you had a model for the error, you could fit it here, but
since you don't mention anything, we'll eyeball it.
The error surface is almost-but-not-quite linear in temperature, but
there is a strange kink in the error between 150 and 200 deg at
T=5, so the question is how accurate is your error data? I think
it's +-10% so a reasonable error approximation could be
.025*sin((ang-40).*pi./180).*(1-(temp-5)/22.5)
hold off; mesh(ang,temp,a(:,2:end)'); hold on ;
mesh(ang,temp,arrayfun(@(ang,temp)
.025*sin((ang-40).*pi./180).*(1-(temp-5)/22.5),ang,temp))
If you get better error data, you could start fitting this (or some
other) ad-hoc 2-d function to your data, using e.g. leasqr() from
the optim package.
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