The easiest way to do this is to create an anonymous function from ‘M_spring’, create the appropriate matrices from ‘r’ and ‘k’ using the meshgrid function, and then evaluate it, then plot using subplot:
%Input Given Parameters
%Lengths
l1=0.006;
l2=0.02;
l3=0.015;
l4=0.02;
l5=0.015;
wing = 0.033;
l = [l1 l2 l3 l4 l5];
L = .04;
%Masses
m1=0.00025;
m2=0.0005;
m3=0.00025;
m4=0.0025;
mass = [m1 m2 m3 m4];
%Pivots
d1x=0.015;
d1y=0.021;
d2x=-0.015;
d2y=0.021;
d = [d1x d1y d2x d2y];
%Inputs
th1dot=10;
th1=90
r =[0.05:.01:.2];
k = [0:5:500];
[Mr,Mk] = meshgrid(r,k); % Create Matrices Of All Combinations Of (r,k)
A_r = -2*d(1)*l(3) + 2*l(1)*l(3).*cos(th1);
B_r = -2*d(2)*l(3) + 2*l(1)*l(3).*sin(th1);
C_r = d(1)^2+d(2)^2+l(1)^2+l(3)^2-l(2)^2-...
2*l(1).*(d(2).*sin(th1)+d(1).*cos(th1));
th3 = 2*atan2(-B_r-sqrt(B_r.^2-C_r.^2+A_r.^2),C_r-A_r);
M_spring_fcn = @(r,k) ((d1x + r.*sin(th3))./(d1y +r.*cos(th3))).*k.*L + m1.*l1.*cos(th1).*th1dot; % Create Anonymous Function From ‘M_spring’
M_spring = M_spring_fcn(Mr,Mk); % Calculate ‘M_spring’
figure(1)
subplot(2,1,1)
plot(r,M_spring)
xlabel('r')
ylabel('M_{spring}')
grid
subplot(2,1,2)
plot(k,M_spring)
xlabel('k')
ylabel('M_{spring}')
grid
The plot:
The upper plot gives a family of curves for various values of ‘k’ as a function of ‘r’, and the lower plot gives a family of curves for various values of ‘r’ as a function of ‘k’.
You probably could benefit by fewer ‘r’ values. A legend for each plot would also help. I have no idea what you’re doing, so I’ll let you sort these.
And May the Fourth be with you!
