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Convert system of first-order differential algebraic equations to equivalent system of differential index 1

`[`

converts
a high-index system of first-order differential algebraic equations `newEqs`

,`newVars`

]
= reduceDAEIndex(`eqs`

,`vars`

)`eqs`

to
an equivalent system `newEqs`

of differential index
1.

`reduceDAEIndex`

keeps the original equations
and variables and introduces new variables and equations. After conversion, `reduceDAEIndex`

checks
the differential index of the new system by calling `isLowIndexDAE`

.
If the index of `newEqs`

is 2 or higher, then `reduceDAEIndex`

issues
a warning.

Check if the following DAE system has a low
(`0`

or `1`

) or high (`>1`

)
differential index. If the index is higher than `1`

,
then use `reduceDAEIndex`

to reduce it.

Create the following system of two differential algebraic equations.
Here, the symbolic functions `x(t)`

, `y(t)`

,
and `z(t)`

represent the state variables of the system.
Specify the equations and variables as two symbolic vectors: equations
as a vector of symbolic equations, and variables as a vector of symbolic
function calls.

syms x(t) y(t) z(t) f(t) eqs = [diff(x) == x + z, diff(y) == f(t), x == y]; vars = [x(t), y(t), z(t)];

Use `isLowIndexDAE`

to check the differential
index of the system. For this system, `isLowIndexDAE`

returns `0`

(`false`

).
This means that the differential index of the system is `2`

or
higher.

isLowIndexDAE(eqs, vars)

ans = logical 0

Use `reduceDAEIndex`

to rewrite the system
so that the differential index is `1`

. The new system
has one additional state variable, `Dyt(t)`

.

[newEqs, newVars] = reduceDAEIndex(eqs, vars)

newEqs = diff(x(t), t) - z(t) - x(t) Dyt(t) - f(t) x(t) - y(t) diff(x(t), t) - Dyt(t) newVars = x(t) y(t) z(t) Dyt(t)

Check if the differential order of the new system is lower than `2`

.

isLowIndexDAE(newEqs, newVars)

ans = logical 1

Reduce the differential index of a system that
contains two second-order differential algebraic equation. Because
the equations are second-order equations, first use `reduceDifferentialOrder`

to
rewrite the system to a system of first-order DAEs.

Create the following system of two second-order DAEs. Here, `x(t)`

, `y(t)`

,
and `F(t)`

are the state variables of the system.
Specify the equations and variables as two symbolic vectors: equations
as a vector of symbolic equations, and variables as a vector of symbolic
function calls.

syms t x(t) y(t) F(t) r g eqs = [diff(x(t), t, t) == -F(t)*x(t),... diff(y(t), t, t) == -F(t)*y(t) - g,... x(t)^2 + y(t)^2 == r^2 ]; vars = [x(t), y(t), F(t)];

Rewrite this system so that all equations become first-order
differential equations. The `reduceDifferentialOrder`

function
replaces the second-order DAE by two first-order expressions by introducing
the new variables `Dxt(t)`

and `Dyt(t)`

.
It also replaces the first-order equations by symbolic expressions.

[eqs, vars] = reduceDifferentialOrder(eqs, vars)

eqs = diff(Dxt(t), t) + F(t)*x(t) diff(Dyt(t), t) + g + F(t)*y(t) - r^2 + x(t)^2 + y(t)^2 Dxt(t) - diff(x(t), t) Dyt(t) - diff(y(t), t) vars = x(t) y(t) F(t) Dxt(t) Dyt(t)

Use `reduceDAEIndex`

to rewrite the system
so that the differential index is `1`

.

[eqs, vars, R, originalIndex] = reduceDAEIndex(eqs, vars)

eqs = Dxtt(t) + F(t)*x(t) g + Dytt(t) + F(t)*y(t) - r^2 + x(t)^2 + y(t)^2 Dxt(t) - Dxt1(t) Dyt(t) - Dyt1(t) 2*Dxt1(t)*x(t) + 2*Dyt1(t)*y(t) 2*Dxt1t(t)*x(t) + 2*Dxt1(t)^2 + 2*Dyt1(t)^2 + 2*y(t)*diff(Dyt1(t), t) Dxtt(t) - Dxt1t(t) Dytt(t) - diff(Dyt1(t), t) Dyt1(t) - diff(y(t), t) vars = x(t) y(t) F(t) Dxt(t) Dyt(t) Dytt(t) Dxtt(t) Dxt1(t) Dyt1(t) Dxt1t(t) R = [ Dytt(t), diff(Dyt(t), t)] [ Dxtt(t), diff(Dxt(t), t)] [ Dxt1(t), diff(x(t), t)] [ Dyt1(t), diff(y(t), t)] [ Dxt1t(t), diff(x(t), t, t)] originalIndex = 3

Use `reduceRedundancies`

to shorten the system.

[eqs, vars] = reduceRedundancies(eqs, vars)

eqs = Dxtt(t) + F(t)*x(t) g + Dytt(t) + F(t)*y(t) - r^2 + x(t)^2 + y(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) - diff(Dyt(t), t) Dyt(t) - diff(y(t), t) vars = x(t) y(t) F(t) Dxt(t) Dyt(t) Dytt(t) Dxtt(t)

The implementation of `reduceDAEIndex`

uses
the Pantelides algorithm. This algorithm reduces higher-index systems
to lower-index systems by selectively adding differentiated forms
of the original equations. The Pantelides algorithm can underestimate
the differential index of a new system, and therefore, can fail to
reduce the differential index to `1`

. In this case, `reduceDAEIndex`

issues
a warning and, for the syntax with four output arguments, returns
the value of `oldIndex`

as `NaN`

.
The `reduceDAEToODE`

function uses more reliable,
but slower Gaussian elimination. Note that `reduceDAEToODE`

requires
the DAE system to be semilinear.

`daeFunction`

| `decic`

| `findDecoupledBlocks`

| `incidenceMatrix`

| `isLowIndexDAE`

| `massMatrixForm`

| `odeFunction`

| `reduceDAEToODE`

| `reduceDifferentialOrder`

| `reduceRedundancies`