Solve differential algebraic equations by using one of the mass
matrix solvers available in MATLAB^{®}. To use this workflow, first
complete steps 1, 2, and 3 from Solve Differential Algebraic Equations (DAEs). Then,
use a mass matrix solver instead of `ode15i`

.

This example demonstrates the use of `ode15s`

or `ode23t`

.
For details on the other solvers, see Choose an ODE Solver (MATLAB) and
adapt the workflow on this page.

From the output of `reduceDAEIndex`

, you
have a vector of equations `DAEs`

and a vector of
variables `DAEvars`

. To use `ode15s`

or `ode23t`

,
you need two function handles: one representing the mass matrix of
a DAE system, and the other representing the right sides of the mass
matrix equations. These function handles form the equivalent mass
matrix representation of the ODE system where *M*(*t*,*y*(*t*))*y*’(*t*) = *f*(*t*,*y*(*t*)).

Find these function handles by using `massMatrixForm`

to
get the mass matrix `M`

and the right sides `F`

.

[M,f] = massMatrixForm(DAEs,DAEvars)

M = [ 0, 0, 0, 0, 0, 0, 0] [ 0, 0, 0, 0, 0, 0, 0] [ 0, 0, 0, 0, 0, 0, 0] [ 0, 0, 0, 0, 0, 0, 0] [ 0, 0, 0, 0, 0, 0, 0] [ 0, 0, 0, 0, -1, 0, 0] [ 0, -1, 0, 0, 0, 0, 0] f = (T(t)*x(t) - m*r*Dxtt(t))/r -(g*m*r - T(t)*y(t) + m*r*Dytt(t))/r r^2 - y(t)^2 - x(t)^2 - 2*Dxt(t)*x(t) - 2*Dyt(t)*y(t) - 2*Dxtt(t)*x(t) - 2*Dytt(t)*y(t) - 2*Dxt(t)^2 - 2*Dyt(t)^2 -Dytt(t) -Dyt(t)

The equations in `DAEs`

can contain symbolic
parameters that are not specified in the vector of variables `DAEvars`

.
Find these parameters by using `setdiff`

on the
output of `symvar`

from `DAEs`

and `DAEvars`

.

pDAEs = symvar(DAEs); pDAEvars = symvar(DAEvars); extraParams = setdiff(pDAEs, pDAEvars)

extraParams = [ g, m, r]

The mass matrix `M`

does not have these extra
parameters. Therefore, convert `M`

directly to a
function handle by using `odeFunction`

.

M = odeFunction(M, DAEvars);

Convert `f`

to a function handle. Specify the
extra parameters as additional inputs to `odeFunction`

.

f = odeFunction(f, DAEvars, g, m, r);

The rest of the workflow is purely numerical. Set parameter values and create the function handle.

g = 9.81; m = 1; r = 1; F = @(t, Y) f(t, Y, g, m, r);

The solvers require initial values for all variables in the
function handle. Find initial values that satisfy the equations by
using the MATLAB `decic`

function.
The `decic`

accepts guesses (which might not satisfy
the equations) for the initial conditions, and tries to find satisfactory
initial conditions using those guesses. `decic`

can
fail, in which case you must manually supply consistent initial values
for your problem.

First, check the variables in `DAEvars`

.

DAEvars

DAEvars = x(t) y(t) T(t) Dxt(t) Dyt(t) Dytt(t) Dxtt(t)

Here, `Dxt(t)`

is the first derivative of `x(t)`

, `Dytt(t)`

is
the second derivative of `y(t)`

, and so on. There
are 7 variables in a `7`

-by-`1`

vector.
Thus, guesses for initial values of variables and their derivatives
must also be `7`

-by-`1`

vectors.

Assume the initial angular displacement of the pendulum is 30°
or `pi/6`

, and the origin of the coordinates is at
the suspension point of the pendulum. Given that we used a radius `r`

of `1`

,
the initial horizontal position `x(t)`

is `r*sin(pi/6)`

.
The initial vertical position `y(t)`

is `-r*cos(pi/6)`

.
Specify these initial values of the variables in the vector `y0est`

.

Arbitrarily set the initial values of the remaining variables
and their derivatives to `0`

. These are not good
guesses. However, they suffice for our problem. In your problem, if `decic`

errors,
then provide better guesses and refer to the `decic`

page.

y0est = [r*sin(pi/6); -r*cos(pi/6); 0; 0; 0; 0; 0]; yp0est = zeros(7,1);

Create an option set that contains the mass matrix `M`

and
initial guesses `yp0est`

, and specifies numerical
tolerances for the numerical search.

opt = odeset('Mass', M, 'InitialSlope', yp0est,... 'RelTol', 10.0^(-7), 'AbsTol' , 10.0^(-7));

Find consistent initial values for the variables and their derivatives by using the
MATLAB
`decic`

function. The first argument of
`decic`

must be a function handle describing the DAE as
`f(t,y,yp) = f(t,y,y') = 0`

. In terms of
`M`

and `F`

, this means ```
f(t,y,yp)
= M(t,y)*yp - F(t,y)
```

.

implicitDAE = @(t,y,yp) M(t,y)*yp - F(t,y); [y0, yp0] = decic(implicitDAE, 0, y0est, [], yp0est, [], opt)

y0 = 0.4771 -0.8788 -8.6214 0 0.0000 -2.2333 -4.1135 yp0 = 0 0.0000 0 0 -2.2333 0 0

Now create an option set that contains the mass matrix `M`

of
the system and the vector `yp0`

of consistent initial
values for the derivatives. You will use this option set when solving
the system.

opt = odeset(opt, 'InitialSlope', yp0);

Solve the system integrating over the time span `0`

≤ `t`

≤ `0.5`

.
Add the grid lines and the legend to the plot. The code uses `ode15s`

.
Instead, you can use `ode23s`

by replacing `ode15s`

with `ode23s`

.

[tSol,ySol] = ode15s(F, [0, 0.5], y0, opt); plot(tSol,ySol(:,1:origVars),'-o') for k = 1:origVars S{k} = char(DAEvars(k)); end legend(S, 'Location', 'Best') grid on

Solve the system for different parameter values by setting the new value and regenerating the function handle and initial conditions.

Set `r`

to `2`

and regenerate
the function handle and initial conditions.

r = 2; F = @(t, Y) f(t, Y, g, m, r); y0est = [r*sin(pi/6); -r*cos(pi/6); 0; 0; 0; 0; 0]; implicitDAE = @(t,y,yp) M(t,y)*yp - F(t,y); [y0, yp0] = decic(implicitDAE, 0, y0est, [], yp0est, [], opt); opt = odeset(opt, 'InitialSlope', yp0);

Solve the system for the new parameter value.

[tSol,ySol] = ode15s(F, [0, 0.5], y0, opt); plot(tSol,ySol(:,1:origVars),'-o') for k = 1:origVars S{k} = char(DAEvars(k)); end legend(S, 'Location', 'Best') grid on