Begginer's Question about matrixes
1 view (last 30 days)
For context I'm developing a basic car speed controller, in which I'm using the changes in altitude to determine a slope which will slow down the car. The controller will speed up the car when it is under the desired speed.
I have defined functions as matrix of (1,instances) many different different parameters, one of which is pressure
I am trying to define pressure as a function of altitude , with altitude being a matrix (1,n) of the following type:
altitude = [a,a,a,a,a,b,c,d,e,f,g,h,iiiiii] %a plane a slope and a plane again
and then I tried to calculate the pressure matrix as function of the altitude in each point as well as the difference in pressure between each step:
press(1,s)=Ps*(1+(Lb/Ts)*altitude(1,s))^(-g0*M/R*Lb); %if youre not familiar with this formula, all the remaining paramters are constants
dp(1,s+1)=k1*(press(1,s+1))-k1*(press(1,s));% k is a gain parameter that I thought might fix this
but when I plot the pressure as function of altitude I get a downwards slope when I'm pretty sure I should be getting a quadratic
additionally the dp matrix should resemble a derivative of the pressure matrix and it is constant throughout time.
As I said I'm a begginer and perhaps this is not the best method for relating parameters with one another, so I'm open to suggestions.
Additionally I have no idea why it doesn't work so If somebody could clear it up for me I would appreciate it!
Thank you in advance
dpb on 16 Nov 2019
Edited: dpb on 16 Nov 2019
"The Matlab way" is to use the vectorized abilities built into operations. NB: the "dot" operator .^ for exponentiation of the vector elementwise instead of the matrix operation without the dot, ^
More than likely your altitude changes are simply not large enough for the power law to "kick in" numerically to see the nonlinearity obviously.
You could, of course, also write the analytic form for the derivative instead of using the differences and be more accurate and lest dependent upon "close enough" to estimate between observed values.