I am trying to plot step responses in MATLAB and cannot figure it out for anything, I have graphed a Bode plot for 3 different k values for the following differential equation in time domain:
d^2y(t)/dt + (v/m)dy(t)/dt + (k/m)y(t) = (k/m)x(t)
in frequency the equation is:
H(jw)=((k/m))/((〖jw)〗^2+(v/m)(jw)+(k/m) )=k/(m(〖jw)〗^2+v(jw)+k)
the values of k are 1, 0.09, 4
The equations to solve for v is as follows:
v=sqrt(2)*sqrt(k*m) where m=1
I now must do the same for step, but am trying to no avail. Can anyone provide any suggestions?
Here is the code for my Bode plot and my attempted but failed step plots:
w=logspace(-2,2,100);
%Creating different vectors based upon K value
%then calculating the frequencey response based upon
%these values
b1=[1];
a1=[1 2^(.5) 1];
H1=freqs(b1,a1,w);
b2=[.09];
a2=[1 (2^.5)*(.09^.5) .09];
H2=freqs(b2,a2,w);
b3=[4];
a3=[1 2*(2^.5) 4];
H3=freqs(b3,a3,w);
%Ploting frequency response on top plot
%with loglog scale
subplot(2,1,1)
loglog(H1,w,'r')
axis([.04 10 .01 10])
hold on
loglog(H2,w,'g')
loglog(H3,w,'c')
xlabel('Omega')
ylabel('Frequency Response')
title('Bode plot with various K values')
legend('H1, K=1','H2, K=.09','H3, K=4')
hold off
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%creating transfer function, how the functions
%respond in time
h1=tf(b1,a1);
h2=tf(b2,a2);
h3=tf(b3,a3);
t=linspace(0,30);
[y1,t1]=step(h1,t);
[y2,t2]=step(h2,t);
[y3,t3]=step(h3,t);
%Ploting step response on bottom plot
%with respect to time
subplot(2,1,2)
plot(t1,abs(y1),'r')
hold on
plot(t2,abs(y2),'g')
plot(t3,abs(y3),'c')
legend('h1, K=1','h2, K=.09','h3, K=4')
xlabel('time(s)')
ylabel('Amplitude')
title('Step response with various K values')
Have you tried using the step function? You already have the coefficients defined in your code above for each of the TFs. step takes in a TF object and gives you the step response in the time domain.
First, take those coefficients and create TF objects. After, run a step response. Using the code you already provided above, do something like this:
b=[1];
a=[1 2^(.5) 1];
% I would personally do: a = [1 sqrt(2) 1];
H1=tf(b, a); % Transfer Function #1
b=[.09];
a=[1 (2^.5)*(.09^.5) .09];
% I would personally do a = [1 sqrt(2*0.09) 0.09];
H2=tf(b, a); % Transfer Function #2
b=[4];
a=[1 2*(2^.5) 4];
% I would personally do a = [1 2*sqrt(2) 4];
H3=tf(b, a); % Transfer Function #3
% Plot the step responses for all three
% Going from 0 to 5 seconds in intervals of 0.01
[y1,t1] = step(H1, 0:0.01:5);
[y2,t2] = step(H2, 0:0.01:5);
[y3,t3] = step(H3, 0:0.01:5);
% Plot the responses
plot(t1, y1, t1, y2, t3, y3)
legend('H1(s)', 'H2(s)', 'H3(s)');
xlabel('Time (s)');
ylabel('Amplitude');
This is the figure I get:
FYI, powering anything to the half is the same as sqrt(). You should consider using that instead to make your code less obfuscated. Judging from your code, it looks like you are trying to modify the frequency of natural oscillations in each second-order underdamped model you are trying to generate while keeping the damping ratio the same. As you increase k, the system should get faster and the steady-state value should also become larger and closer towards 1 - ensuring that you compensate for the DC gain of course. (I'm a former instructor on automatic control systems).
Related
In my project, I used a generic cosine function to fit my data:
cos_fun = #(p, theta) p(1) + p(2) * cos(theta - p(3))
p = nlinfit(x,y,cos_fun,[1 1 0])
As a result, p has three values, which are y-offset, amplitude and phase.
Can I draw a smooth cosine curve using these three parameters?
TL;DR: It is possible to both fit and plot the curve, with and without requiring toolboxes. All cases presented below.
Plotting
Plotting the function follows directly from the function used to obtain your parameters using plot(). Notice that you control the smoothness of the plotted function based on the step size for the domain (see step below).
In the figure, the results obtained from the nlinfit() (Toolbox required) are the same as "SSE" obtained without a toolbox using fminsearch().
% Plot (No toolbox Required)
step = 0.01; % smaller is smoother
Xrng = 0:step:12;
figure, hold on, box on
plot(Xdata,Ydata,'b.','DisplayName','Data')
plot(Xrng,cos_fun(p_SSE,Xrng),'r--','DisplayName','SSE')
plot(Xrng,cos_fun(p_SAE,Xrng),'k--','DisplayName','SAE')
legend('show')
As pointed out in the comment by #Daniel, you can also make the plot with nlintool() but this requires the Statistics Toolbox.
nlintool(Xdata,Ydata,cos_fun,[1 1 0]) % toolbox required
Fitting
Using nlinfit(): (Statistics Toolbox Required)
pNL = nlinfit(Xdata,Ydata,cos_fun,[1 1 0]) % same as SSE approach below
A Toolbox Free Approach:
You can construct a convex error function to minimize and return the global optimum using fminsearch() as a down and dirty approach. For example, the sum of squared error or the sum of absolute error will be convex.
% MATLAB R2019a
% Generate Example Data
sigma = 0.5; % increase this for more variable data (more noise)
Xdata = [repmat(1:10,1,4)].';
Ydata = cos(Xdata)+sigma*randn(length(Xdata),1);
% Function Evaluation
cos_fun=#(p,x) p(1) + p(2).*cos(x-p(3));
% Error Functions
SSEh =#(p) sum((cos_fun(p,Xdata)-Ydata).^2); % sum of squared error
SAEh =#(p) sum(abs(cos_fun(p,Xdata)-Ydata)); % sum of absolute error
Of course, these will give you different errors for the same parameter.
% Test
SSEh([1 1 0])
SAEh([1 1 0])
But you then call fminsearch() given an initial guess for the parameters, p0, to obtain the parameters that minimize your chosen error function. Since SSEh and SAEh are both convex with respect to p, there's no need to do this multiple times and save the best one since for every p0, you'll get the same answer.
p0 = [1 1 0.25]; % Initial starting point
[p_SSE, SSE] = fminsearch(SSEh,p0)
[p_SAE, SAE] = fminsearch(SAEh,p0)
You fit slightly different curves depending on the error function.
Notice that SSEh(pNL) and SSEh(p_SSE) are the same since pNL equals p_SSE since nlinfit() estimates the coefficients "using iterative least squares estimation."
I am trying to fit a distribution curve to the histogram of some data. (I have used some model data here instead because it is difficult to upload the actual data. I have included the complete code after my question.)
Because the histogram looks normally distributed when I plotted the x-axis in logscale, I transform the data first before fitting a normal distribution to it and I got the following results:
>>pdn=fitdist(log(data),'Normal')
pdn =
Normal distribution
mu = -0.334458 [-0.34704, -0.321876]
sigma = 0.351478 [0.342804, 0.360605]
When I plotted out the pdf with the histogram, I got this:
The result seems reasonable to me. Then I discovered that in the Matlab fitdist(), it already has a 'Lognormal' option and I don't really need the transform my data first and this is what I got:
>>pdln = fitdist(data,'Lognormal')
pdln =
Lognormal distribution
mu = -0.334458 [-0.34704, -0.321876]
sigma = 0.351478 [0.342804, 0.360605]
Exactly the same mean and standard deviation as I have got before. However, when I plotted it out with the histogram, I got a different curve:
This curve fits better to the data but the positions of the mean and the mean+/-std points are not as I have expected (i.e. mean at the peak and the mean+/-std at the same levels).
Which come to my question, why would fitdist(data,'Lognormal') give the same result as fitdist(log(data),'Normal') but a different plot? I have looked through the Matlab help pages and I still could not understand why, or where are my mistakes, please help.
My aim for all this is to get some numerical parameters about the distributions of my data under different conditions and compare them to see if there is any difference. At the moment, I am not certain which way would give me reliable estimates of the means and standard deviations.
The code for the graphs is below:
%random data in lognormal distribution
mu=-0.335742;
sigma=0.35228;
data=lognrnd(mu,sigma,[3000 1]);
%make histogram
interval=0.1;
svalue=sort(data);
bx(1)=interval/2;
i=2;
while bx(i-1)<=max(svalue)
bx(i)=bx(i-1)+interval;
i=i+1;
end
by=hist(svalue,bx);
subplot(211)
h = bar(bx,by,'hist');
set(h,'FaceColor',[.9 .9 .9]);
set(gca,'xlim',[0.05 10]);
xticks=[0.05 0.1 0.2 0.5 1 2 5 10];
set(gca,'xscale','log','xminortick','on')
set(gca,'xtick',xticks)
ylabel('counts')
subplot(212)
h = bar(bx,by,'hist');
set(h,'FaceColor',[.9 .9 .9]);
set(gca,'xlim',[0.05 10]);
xticks=[0.05 0.1 0.2 0.5 1 2 5 10];
set(gca,'xscale','log','xminortick','on')
set(gca,'xtick',xticks)
ylabel('counts')
% fit distribution curves
pdf_x = 0:0.01:max(data);
max_by=max(by); % for scaling the pdf to the histogram
% case 1 - PDF fitted using fitdist(log(data),'Normal')
subplot(211)
hold on
pdn = fitdist(log(data),'Normal')
pdf_y = pdf(pdn,log(pdf_x));
h1=plot(pdf_x,pdf_y./max(pdf_y).*max_by,'-k');
range=[exp(pdn.mu-pdn.sigma) exp(pdn.mu+pdn.sigma)];
h2=plot(exp(pdn.mu),pdf(pdn,(pdn.mu))./max(pdf_y).*max_by,'sk') ;
h3=plot(range,pdf(pdn,log(range))./max(pdf_y).*max_by,'ok') ;
title('PDF fitted using fitdist(log(data),''Normal'')');
legend([h1 h2 h3],'pdf','mean','meam+/-std');
% case 2 - PDF fitted using fitdist(data,'Lognormal')
subplot(212)
hold on
pdln = fitdist(data,'Lognormal')
pdf_y = pdf(pdln,pdf_x);
h1=plot(pdf_x,pdf_y./max(pdf_y).*max_by,'-b');
range=[exp(pdln.mu-pdln.sigma) exp(pdln.mu+pdln.sigma)];
h2=plot(exp(pdln.mu),pdf(pdln,exp(pdln.mu))./max(pdf_y).*max_by,'sb');
h3=plot(range,pdf(pdln,range)./max(pdf_y).*max_by,'ob') ;
title('PDF fitted using fitdist(data,''Lognormal'')');
legend([h1 h2 h3],'pdf','mean','meam+/-std');
I have 3 singals and I'm trying to plot their phasors and their sum. I need to plot them end to end to demonstrate phasor addition. That is, the first phasor must start from the origin. The second phasor must start from the end of the first phasor. The third phasor must start from the end of the second one. In this way, the end point of the third phasor is the resulting phasor (considering that it starts at the origin). Horizontal and vertical axes are the real and imaginary axes, respectively in range of [-30, 30].
I just started using matlab today and this is due the night. I tried using plot, plot2, plot3, compass, and several ways but with all of them i failed. Compass was the closest to success.
I have amplitude and phase values of each phasor.
So how can I accomplish this task? Can you help me to draw two of phasors?
Any help is appreciated.
Thank you!
Related Example: from http://fourier.eng.hmc.edu/e84/lectures/ch3/node2.html
[example by spektre]
The following example should get you started:
First, the three phasors are defined.
% Define three complex numbers by magnitude and phase
ph1 = 20*exp(1i*0.25*pi);
ph2 = 10*exp(1i*0.7*pi);
ph3 = 5*exp(1i*1.2*pi);
Then, using cumsum, a vector containing ph1, ph1+ph2, ph1+ph2+ph3 is calculated.
% Step-wise vector sum
vecs = cumsum([ph1; ph2; ph3]);
vecs = [0; vecs]; % add origin as starting point
The complex numbers are plotted by real and imaginary part.
% Plot
figure;
plot(real(vecs), imag(vecs), '-+');
xlim([-30 30]);
ylim([-30 30]);
xlabel('real part');
ylabel('imaginary part');
grid on;
This produces the following figure:
figure(1); hold on;
ang = [0.1 0.2 0.7] ; % Angles in rad
r = [1 2 4] ; % Vector of radius
start = [0 0]
for i=1:numel(r)
plot([start(1) start(1)+r(i)*cos(ang(i))],[start(2) start(2)+r(i)*sin(ang(i))],'b-+')
start=start+[r(i)*cos(ang(i)) r(i)*sin(ang(i))]
end
plot([0 start(1)],[0 start(2)],'r-')
How can I plot amplitude of transfer function in three dimension (for instance to check poles and zeros on graph) ?
Suppose this is my transfer function:
My code:
b = [6 -10 2];
a = [1 -3 2];
[x, y] = meshgrid(-3:0.1:3);
z = x+y*j;
res = (polyval(b, z))./(polyval(a,z));
surf(x,y, abs(res));
Is it correct? I'd also like to know is it possible to mark unit circle on plot?
I think it's correct. However, you're computing H(z^-1), not H(z). Is that you want to do? For H(z), just reverse the entries in a from left to right (with fliplr), and do the same to b:
res = (polyval(fliplr(b), z))./(polyval(fliplr(a),z));
To plot the unit circle you can use rectangle. Seriously :-) It has a 'Curvature' property which can be set to generate a circle.
It's best if you use imagesc instead of surf to make the circle clearly visible. You will get a view from above, where color represents height (value of abs(H)):
imagesc(-3:0.1:3,-3:0.1:3, abs(res));
hold on
rectangle('curvature', [1 1], 'position', [-1 -1 2 2], 'edgecolor', 'w');
axis equal
I have never in my whole life heard of a 3D transfer function, it doesn't make sense. I think you are completely wrong: z does not represent a complex number, but the fact that your transfer function is a discrete one, rather than a continuous one (see the Z transform for more details).
The correct way to do this in MATLAB is to use the tf function, which requires the Control System Toolbox (note that I have assumed your discrete sample time to be 0.1s, adjust as required):
>> b = [6 -10 2];
a = [1 -3 2];
>> sys = tf(b,a,0.1,'variable','z^-1')
sys =
6 - 10 z^-1 + 2 z^-2
--------------------
1 - 3 z^-1 + 2 z^-2
Sample time: 0.1 seconds
Discrete-time transfer function.
To plot the transfer function, use the bode or bodeplot function:
bode(sys)
For the poles and zeros, simply use the pole and zero functions.
i want write a script to plot a graph for the transfer function [H(f)] for a band pass filter, |H(f)| against frequency and the phase of H(f) (degrees) against frequency, im very new to matlab so the syntax is not 100%, im getting confused because everything is auto formatted in matrix form.
below is my script:
% RCL circuit: band-pass filter
R=55590; L=0.9571; C=48.811*10.^(-9); % values of the Resistor and Capacitor
f=(0:60e3); w=2*pi*f; % frequency (f) range of measurements
H=(R./(sqrt(R^2+(w*L-(1./(w*C))).^2))); % Transfer Function
% Magnitude (absolute value) of the transfer function
plot(f,abs(H),'LineWidth',2); grid on; hold on
xlabel('Frequency [Hz]','FontSize',20); ylabel('|H(f)|','FontSize',20)
figure
plot(f,angle(H)*180/pi,'LineWidth',2); grid on; hold on
xlabel('Frequency [Hz]','FontSize',18);
ylabel('phase of H(f) [degrees]','FontSize',20)
this is the transfer function formula im using
below is another pic of what my experimental results were and an expected graph, i just dont understand why MATLAB isnt ploting what i want?
Are you aware of the bodeplot function?
The transfer function for a simple second order bandpass is:
You just need to insert your values into Matlab's tf function and plot it with bodeplot:
R = 55590;
L = 0.9571;
C = 48.811*10.^(-9);
% tf creates transfer function object
sys = tf( [R*C 0] , [L*C R*C 1]); % [R*C 0] vector of numerator coeffcients
% R*C*s + 0*1
% [L*C R*C 1] vector of denominator coeff.
% L*C*s^2 + R*C*s + 0*1
bodeplot(sys) % plot command to plot frequency response
% of magnitude and phase
and it plots:
Alternatively, if you need more outputs like magnitude and phase as variables, use bode, the plot is equal. But bodeplot offers more additional plot customization options.
Regarding your comment, that you need a linear axis:
you just need to add the following lines before the plot command:
P = bodeoptions; % handle to plot options
P.MagScale = 'linear';
P.MagUnits = 'abs';
bodeplot(sys,P) % plot command with custom options
so it looks as follows:
For adjusting the frequency axis limits, use:
P.XLim = [1 60e3];
or analogue for the magnitude:
P.YLim = [0 1];
I would advise against a linear frequency axis, but if you REALLY want it, you can use:
P.FreqScale = 'linear';
P.FreqUnits = 'Hz'; % optional
If you want to plot your experimental data together with the plot above, follow this example.
Use bode to get equally formatted data from your transfer function, like your experimental data and use semilogx to plot it.
freqVec = logspace(-1, 3, 5000);
[mag, phs] = bode(sys, freqVec * (2*pi));
mag = db(mag(:));
phs = phs(:);
figure;
subplot(211)
semilogx(freqVec, mag); hold on
semilogx(freqVec, experimentalDataMagnitude); hold off
grid on
title('System Bode Plot')
ylabel('Magnitude (dB)')
subplot(212)
semilogx(freqVec, phs); hold on
semilogx(freqVec, experimentalDataPhase); hold off
grid on
ylabel('Phase (deg)')
xlabel('Frequency (Hz)')