How to find and fit the objective function for a damped oscillation?
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Piyumal Samarathunga
le 28 Nov 2022
Commenté : Star Strider
le 16 Fév 2023
The excel file contains force data of a damped oscillation system. In order to find the natural frequency of the system I am trying to fit the graph with an objective function as follows.
I have defined the function of the osillation.
function F = forcesim(t,A,omega,phi,C,K)
F=A*cos(omega*t+phi)*exp(-t*C) + K;
end
I have tried with a code to find the estimated frequency as follows.
dataset = readtable ("F300.xlsx");
Force = dataset.Force2;
Time = dataset.Time2;
plot(Time,Force)
xlabel("Time (s)")
ylabel("Force (N)")
grid on
ind = Time>0.3 & Time<1.3;
[pks,locs] = findpeaks(Force(ind),Time(ind),'MinPeakProminence',1);
peaks = [pks locs]
% visualizse the selected peaks and label them.
hold on
plot(locs,pks,'v')
text(locs+.02,pks,num2str((1:numel(pks))'))
hold off
% calculate the approximate frequency
meanCycle = length(pks)/(locs(end)-locs(1))
Hence, that frequency is an estimated one, and therefore I am planning to find the frequency of the graph by matching the objective function mentioned above.
It would be really appreciated if you could tell me how to do that and find the frequency ω.
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Star Strider
le 29 Nov 2022
T1 = readtable('https://www.mathworks.com/matlabcentral/answers/uploaded_files/1211598/F300.xlsx')
x = T1.Time2;
y = T1.Force2;
start = find(y > 1, 1, 'first') % Find Offset
y = y(start:end);
x = x(start:end);
y = detrend(y,1); % Remove Linear Trend
yu = max(y);
yl = min(y);
yr = (yu-yl); % Range of ‘y’
yz = y-yu+(yr/2);
zci = @(v) find(v(:).*circshift(v(:), [-1 0]) <= 0); % Returns Approximate Zero-Crossing Indices Of Argument Vector
zt = x(zci(y));
per = 2*mean(diff(zt)); % Estimate period
ym = mean(y); % Estimate offset
fit = @(b,x) b(1) .* exp(b(2).*x) .* (sin(2*pi*x./b(3) + 2*pi/b(4))) + b(5); % Objective Function to fit
fcn = @(b) norm(fit(b,x) - y); % Least-Squares cost function
[s,nmrs] = fminsearch(fcn, [yr; -10; per; -1; ym]) % Minimise Least-Squares
xp = linspace(min(x),max(x), numel(x)*10);
figure(3)
plot(x,y,'b', 'LineWidth',1.5)
hold on
plot(xp,fit(s,xp), '-r')
hold off
grid
xlabel('Time')
ylabel('Amplitude')
legend('Original Data', 'Fitted Curve')
xlim([min(x) 1])
text(0.3*max(xlim),0.7*min(ylim), sprintf('$y = %.3f\\cdot e^{%.1f\\cdot x}\\cdot sin(2\\pi\\cdot x\\cdot %.0f%.3f)%+.1f$', [s(1:2); 1./s(3:4); s(5)]), 'Interpreter','latex')
format short eng
grid
mdl = fitnlm(x(:),y(:),fit,s) % Get Statistics
.
8 commentaires
Star Strider
le 16 Fév 2023
As always, my pleasure!
The Global Optimization Toolbox has functions such as ga that can make the parameter space search easier and more efficient.
Plus de réponses (1)
Matt J
le 28 Nov 2022
Modifié(e) : Matt J
le 28 Nov 2022
Using fminspleas from the File Exchange,
[t,F]= readvars ("https://www.mathworks.com/matlabcentral/answers/uploaded_files/1211598/F300.xlsx");
region=t>=0.2 &t<=0.8;
F=F(region);
t=t(region);
flist={@(p,x)cos(p(1)*t+p(2)).*exp(-t*p(3)),1 };
p0=[0.15,0,1];
[p,AK]=fminspleas(flist,p0,t,F);
f=@(t) AK(1)*cos(p(1)*t+p(2)).*exp(-t*p(3)) +AK(2);
plot(t,F,'o',t,f(t)); legend('Data','Fit')
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