Solving System of Second Order Ordinary Differential Equation in Matlab - matlab

Introduction
I am using Matlab to simulate some dynamic systems through numerically solving systems of Second Order Ordinary Differential Equations using ODE45. I found a great tutorial from Mathworks (link for tutorial at end) on how to do this.
In the tutorial the system of equations is explicit in x and y as shown below:
x''=-D(y) * x' * sqrt(x'^2 + y'^2)
y''=-D(y) * y' * sqrt(x'^2 + y'^2) + g(y)
Both equations above have form y'' = f(x, x', y, y')
Question
However, I am coming across systems of equations where the variables can not be solved for explicitly as shown in the example. For example one of the systems has the following set of 3 second order ordinary differential equations:
y double prime equation
y'' - .5*L*(x''*sin(x) + x'^2*cos(x) + (k/m)*y - g = 0
x double prime equation
.33*L^2*x'' - .5*L*y''sin(x) - .33*L^2*C*cos(x) + .5*g*L*sin(x) = 0
A single prime is first derivative
A double prime is second derivative
L, g, m, k, and C are given parameters.
How can Matlab be used to numerically solve a set of second order ordinary differential equations where second order can not be explicitly solved for?
Thanks!

Your second system has the form
a11*x'' + a12*y'' = f1(x,y,x',y')
a21*x'' + a22*y'' = f2(x,y,x',y')
which you can solve as a linear system
[x'', y''] = A\f
or in this case explicitly using Cramer's rule
x'' = ( a22*f1 - a12*f2 ) / (a11*a22 - a12*a21)
y'' accordingly.
I would strongly recommend leaving the intermediate variables in the code to reduce chances for typing errors and avoid multiple computation of the same expressions.
Code could look like this (untested)
function dz = odefunc(t,z)
x=z(1); dx=z(2); y=z(3); dy=z(4);
A = [ [-.5*L*sin(x), 1] ; [.33*L^2, -0.5*L*sin(x)] ]
b = [ [dx^2*cos(x) + (k/m)*y-g]; [-.33*L^2*C*cos(x) + .5*g*L*sin(x)] ]
d2 = A\b
dz = [ dx, d2(1), dy, d2(2) ]
end

Yes your method is correct!
I post the following code below:
%Rotating Pendulum Sym Main
clc
clear all;
%Define parameters
global M K L g C;
M = 1;
K = 25.6;
L = 1;
C = 1;
g = 9.8;
% define initial values for theta, thetad, del, deld
e_0 = 1;
ed_0 = 0;
theta_0 = 0;
thetad_0 = .5;
initialValues = [e_0, ed_0, theta_0, thetad_0];
% Set a timespan
t_initial = 0;
t_final = 36;
dt = .01;
N = (t_final - t_initial)/dt;
timeSpan = linspace(t_final, t_initial, N);
% Run ode45 to get z (theta, thetad, del, deld)
[t, z] = ode45(#RotSpngHndl, timeSpan, initialValues);
%initialize variables
e = zeros(N,1);
ed = zeros(N,1);
theta = zeros(N,1);
thetad = zeros(N,1);
T = zeros(N,1);
V = zeros(N,1);
x = zeros(N,1);
y = zeros(N,1);
for i = 1:N
e(i) = z(i, 1);
ed(i) = z(i, 2);
theta(i) = z(i, 3);
thetad(i) = z(i, 4);
T(i) = .5*M*(ed(i)^2 + (1/3)*L^2*C*sin(theta(i)) + (1/3)*L^2*thetad(i)^2 - L*ed(i)*thetad(i)*sin(theta(i)));
V(i) = -M*g*(e(i) + .5*L*cos(theta(i)));
E(i) = T(i) + V(i);
end
figure(1)
plot(t, T,'r');
hold on;
plot(t, V,'b');
plot(t,E,'y');
title('Energy');
xlabel('time(sec)');
legend('Kinetic Energy', 'Potential Energy', 'Total Energy');
Here is function handle file for ode45:
function dz = RotSpngHndl(~, z)
% Define Global Parameters
global M K L g C
A = [1, -.5*L*sin(z(3));
-.5*L*sin(z(3)), (1/3)*L^2];
b = [.5*L*z(4)^2*cos(z(3)) - (K/M)*z(1) + g;
(1/3)*L^2*C*cos(z(3)) + .5*g*L*sin(z(3))];
X = A\b;
% return column vector [ed; edd; ed; edd]
dz = [z(2);
X(1);
z(4);
X(2)];

Related

system of equation Runge-Kutta 4th order for system of equation using matlab [duplicate]

This question already has answers here:
Solve a system of equations with Runge Kutta 4: Matlab
(2 answers)
Closed 4 years ago.
I need to do matlab code to solve the system of equation by using Runge-Kutta method 4th order but in every try i got problem and can't solve
the derivative is
(d^2 y)/dx^(2) +dy/dx-2y=0
, h=0.1 Y(0)=1 , dy/dx (0)=-2
{clear all, close all, clc
%{
____________________TASK:______________________
Solve the system of differential equations below
in the interval 0<x<1, with stepsize h = 0.1.
y= y1 y(0)=0
y3= 2y1-y2 y2(0)=-2
_______________________________________________
%}
h = 0.1;
x = 0:h:1
N = length(x);
y = zeros(N,1);
y3 = zeros(N,1);
g = #(x, y, y1, y2) y1;
f = #(x, y, y1, y2) 2*y1-y2;
y1(1) = 0;
y2(1) =-2;
for i = 1:(N-1)
k_1 = x(i)+y(i)
k_11=g(x(i),y,y(i))
k_2 = (x(i)+h/2)+(y(i)+0.5*h*k_1)
k_22=g((x(i)+0.5*h),y,(y(i)+0.5*h*k_11))
k_3 = (x(i)+h/2)+(y(i)+0.5*h*k_2)
k_33=g((X(i)+0.5*h),y,(y(i)+0.5*h*k_22));
k_4 = (x(i)+h)+(y(i)+h*k_33)
k_44=g((x(i)+h),y,(y(i)+k_33*h));
y3(i+1) = y(i) + (1/6)*(k_1+2*k_2+2*k_3+k_4)*h
y3(:,i)=y;
end
Answer_Matrix = [x' y3 ];}
You used functions, that's not really necessary, but it might be easier that way to see the formula more clearly. In your functions however, you list arguments that used present in the function. That's not needed, and creates unwanted overhead.
In your initial conditions you should use y and y3, since that are the ones you use in the loop. Also in the first condition you've made a typo.
In the loop you forget to call the function f, and to update the y vector.
Making these changes in your code results in the following:
h = 0.1;
x = 0:h:1;
N = length(x);
y = zeros(N,1);
y3 = zeros(N,1);
g = #(y2) y2;
f = #(y1, y2) 2*y1-y2;
y(1) = 1;
y3(1) = -2;
for i = 1:(N-1)
k_1 = f(y(i), y3(i));
k_11 = g(y3(i));
k_2 = f(y(i)+0.5*h*k_1, y3(i) +0.5*h*k_11);
k_22 = g((y3(i)+0.5*h*k_11));
k_3 = f(y(i)+0.5*h*k_2, y3(i) +0.5*h*k_22);
k_33 = g((y3(i)+0.5*h*k_22));
k_4 = f(y(i)+h*k_3, y3(i) +h*k_33);
k_44 = g((y3(i)+h*k_33));
y3(i+1) = y3(i) + (1/6)*(k_1+2*k_2+2*k_3+k_4)*h ;
y(i+1) = y(i) + (1/6)*(k_11+2*k_22+2*k_33+k_44)*h ;
end
Answer_Matrix = [x' y];
% solution of DE is exp(-2x) and is plotted as reference
plot(x,y,x,exp(-2*x))
As mentioned before, you can also solve this without the use of functions:
h = .1;
x = 0:h:1;
N = length(x);
% allocate memory
y = zeros(N,1);
z = zeros(N,1);
% starting values
y(1) = 1;
z(1) = -2;
for i=1:N-1
ky1 = z(i);
kz1 = -z(i) + 2*y(i);
ky2 = z(i) + h/2*kz1;
kz2 = -z(i) - h/2*kz1 + 2*y(i) + 2*h/2*ky1;
ky3 = z(i) + h/2*kz2;
kz3 = -z(i) - h/2*kz2 + 2*y(i) + 2*h/2*ky2;
ky4 = z(i) + h*kz3;
kz4 = -z(i) - h*kz3 + 2*y(i) + 2*h*ky3;
y(i+1) = y(i) + h/6*(ky1 + 2*ky2 + 2*ky3 + ky4);
z(i+1) = z(i) + h/6*(kz1 + 2*kz2 + 2*kz3 + kz4);
end
% exp(-2*x) is solution of DE and is plotted as reference
plot(x,y,x,exp(-2*x))

How to iterate over functions?

I would like to apply loop over a function. I have the following "mother" code:
v = 1;
fun = #root;
x0 = [0,0]
options = optimset('MaxFunEvals',100000,'MaxIter', 10000 );
x = fsolve(fun,x0, options)
In addition, I have the following function in a separate file:
function D = root(x)
v = 1;
D(1) = x(1) + x(2) + v - 2;
D(2) = x(1) - x(2) + v - 1.8;
end
Now, I would like to find roots when v = sort(rand(1,1000)). In other words, I would like to iterate over function for each values of v.
You will need to modify root to accept an additional variable (v) and then change the function handle to root to an anonymous function which feeds in the v that you want
function D = root(x, v)
D(1) = x(1) + x(2) + v - 2;
D(2) = x(1) - x(2) + v - 1.8;
end
% Creates a function handle to root using a specific value of v
fun = #(x)root(x, v(k))
Just in case that equation is your actual equation (and not a dummy example): that equation is linear, meaning, you can solve it for all v with a simple mldivide:
v = sort(rand(1,1000));
x = [1 1; 1 -1] \ bsxfun(#plus, -v, [2; 1.8])
And, in case those are not your actual equations, you don't need to loop, you can vectorize the whole thing:
function x = solver()
options = optimset('Display' , 'off',...
'MaxFunEvals', 1e5,...
'MaxIter' , 1e4);
v = sort(rand(1, 1000));
x0 = repmat([0 0], numel(v), 1);
x = fsolve(#(x)root(x,v'), x0, options);
end
function D = root(x,v)
D = [x(:,1) + x(:,2) + v - 2
x(:,1) - x(:,2) + v - 1.8];
end
This may or may not be faster than looping, it depends on your actual equations.
It may be slower because fsolve will need to compute a Jacobian of 2000×2000 (4M elements), instead of 2×2, 1000 times (4k elements).
But, it may be faster because the startup cost of fsolve can be large, meaning, the overhead of many calls may in fact outweigh the cost of computing the larger Jacobian.
In any case, providing the Jacobian as a second output will speed everything up rather enormously:
function solver()
options = optimset('Display' , 'off',...
'MaxFunEvals', 1e5,...
'MaxIter' , 1e4,...
'Jacobian' , 'on');
v = sort(rand(1, 1000));
x0 = repmat([1 1], numel(v), 1);
x = fsolve(#(x)root(x,v'), x0, options);
end
function [D, J] = root(x,v)
% Jacobian is constant:
persistent J_out
if isempty(J_out)
one = speye(numel(v));
J_out = [+one +one
+one -one];
end
% Function values at x
D = [x(:,1) + x(:,2) + v - 2
x(:,1) - x(:,2) + v - 1.8];
% Jacobian at x:
J = J_out;
end
vvec = sort(rand(1,2));
x0 = [0,0];
for v = vvec,
fun = #(x) root(v, x);
options = optimset('MaxFunEvals',100000,'MaxIter', 10000 );
x = fsolve(fun, x0, options);
end
with function definition:
function D = root(v, x)
D(1) = x(1) + x(2) + v - 2;
D(2) = x(1) - x(2) + v - 1.8;
end

The Fastest Method of Solving System of Non-linear Equations in MATLAB

Assume we have three equations:
eq1 = x1 + (x1 - x2) * t - X == 0;
eq2 = z1 + (z1 - z2) * t - Z == 0;
eq3 = ((X-x1)/a)^2 + ((Z-z1)/b)^2 - 1 == 0;
while six of known variables are:
a = 42 ;
b = 12 ;
x1 = 316190;
z1 = 234070;
x2 = 316190;
z2 = 234070;
So we are looking for three unknown variables that are:
X , Z and t
I wrote two method to solve it. But, since I need to run these code for 5.7 million data, it become really slow.
Method one (using "solve"):
tic
S = solve( eq1 , eq2 , eq3 , X , Z , t ,...
'ReturnConditions', true, 'Real', true);
toc
X = double(S.X(1))
Z = double(S.Z(1))
t = double(S.t(1))
results of method one:
X = 316190;
Z = 234060;
t = -2.9280;
Elapsed time is 0.770429 seconds.
Method two (using "fsolve"):
coeffs = [a,b,x1,x2,z1,z2]; % Known parameters
x0 = [ x2 ; z2 ; 1 ].'; % Initial values for iterations
f_d = #(x0) myfunc(x0,coeffs); % f_d considers x0 as variables
options = optimoptions('fsolve','Display','none');
tic
M = fsolve(f_d,x0,options);
toc
results of method two:
X = 316190; % X = M(1)
Z = 234060; % Z = M(2)
t = -2.9280; % t = M(3)
Elapsed time is 0.014 seconds.
Although, the second method is faster, but it still needs to be improved. Please let me know if you have a better solution for that. Thanks
* extra information:
if you are interested to know what those 3 equations are, the first two are equations of a line in 2D and the third equation is an ellipse equation. I need to find the intersection of the line with the ellipse. Obviously, we have two points as result. But, let's forget about the second answer for simplicity.
My suggestion it's to use the second approce,which it's the recommended by matlab for nonlinear equation system.
Declare a M-function
function Y=mysistem(X)
%X(1) = X
%X(2) = t
%X(3) = Z
a = 42 ;
b = 12 ;
x1 = 316190;
z1 = 234070;
x2 = 316190;
z2 = 234070;
Y(1,1) = x1 + (x1 - x2) * X(2) - X(1);
Y(2,1) = z1 + (z1 - z2) * X(2) - X(3);
Y(3,1) = ((X-x1)/a)^2 + ((Z-z1)/b)^2 - 1;
end
Then for solving use
x0 = [ x2 , z2 , 1 ];
M = fsolve(#mysistem,x0,options);
If you may want to reduce the default precision by changing StepTolerance (default 1e-6).
Also for more increare you may want to use the jacobian matrix for greater efficencies.
For more reference take a look in official documentation:
fsolve Nonlinear Equations with Analytic Jacobian
Basically giving the solver the Jacobian matrix of the system(and special options) you can increase method efficency.

Solving Multiple Equations For Repeated Times With Different Initial Conditions Matlab

I have the following set of values in an array.
a = [a(1) a(2) ... a(1907)]
Gamma(1)= (u*f(1))+(r*a(1))
u and r are constant and f(n) changes during each step and its initial value is f(1) = zero.
f(n) next values will be generated from solving these equations.
h(1) = x(1) + Gamma(1) for which x(1)=0 and in next steps is constant. (c)
Z(1)= constant(T) * h(1)
f(2) = constant(G) * Z(1)
These steps will repeated 1907 times. Any idea what should I do at all?
You can input your initial conditions into a very simple for loop.
% a, u, r, T, G are assumed available.
f = zeros(1908, 1);
Z = zeros(1907 ,1);
Gamma = zeros(1907, 1);
x = [0; c*ones(1906, 1)];
for ii = 1:1907
Gamma(ii) = u*f(ii) + r*a(ii);
h(ii) = x(ii) + Gamma(ii);
Z(ii) = T*h(ii);
f(ii+1) = G*Z(ii);
end

Forward Euler to solve a system of first order ODEs in Matlab

I have two first order ODEs that I got from a second order ODE:
y(0)=1
y'(0)=-1/3
u1'=u2
u2=u/9-(pi*u1*e^(x/3)*(2u2*sin(pi*x)+pi*u1cos(pi*x))
u1(0)=y(0)=1
u2(0)=y'(0)=-1/3
My question is how to set up forward Euler? I have that:
n=[0:0.01:2];
h=2./n;
Our equations are:
u1' = u2
u2' = u1/9 - \pi u1 exp(x/3)(2u2 sin(\pi x) + \pi u1 cos(\pi x))
Now the Euler Method for solving an y' = f(x,y) is:
y_{n+1} = y_{n} + h * f(x_n, y_n)
As MATLAB code, we could write this as:
h = 0.01; % Choose a step size
x = [0:h:2]; % Set up x
u = zeros(length(x),2);
u(1,:) = [1; -1/3]; % Initial Conditions for y
for ii = 2:length(x)
u(ii,:) = u(ii-1,:) + h * CalculateDeriv(x(ii-1),u(ii-1,:)); % Update u at each step
end
function deriv = CalculateDerivative(x,u)
deriv = zeros(2,1);
deriv(1) = u(2);
deriv(2) = u(1)/9 - pi*u(1)*exp(x/9)*(2*u(2)*sin(pi*x) + pi*u(1)*cos(pi*x))
end