# xelim

## Description

simplifies the state-space model `rsys`

= xelim(`sys`

,`elim`

)`sys`

by eliminating the states
specified in the vector `elim`

. The full state vector
*x* is partitioned as *x* =
[*x*_{1};*x*_{2}]
where *x*_{1} is the reduced state vector and
*x*_{2} is eliminated.

This function is useful to eliminate states known to settle quickly (fast modes) or
contribute little to the input/output map. When you don’t know which states to eliminate,
use `reducespec`

and
the model-order reduction workflow.

## Examples

### Order Reduction by Matched-DC-Gain and Direct-Deletion Methods

Consider the following continuous fourth-order model.

$$h(s)=\frac{{s}^{3}+11{s}^{2}+36s+26}{{s}^{4}+14.6{s}^{3}+74.96{s}^{2}+153.7s+99.65}.$$

To reduce its order, first compute a balanced state-space realization with `balreal`

.

h = tf([1 11 36 26],[1 14.6 74.96 153.7 99.65]); [hb,g] = balreal(h);

Examine the Gramians.

g'

`ans = `*1×4*
0.1394 0.0095 0.0006 0.0000

The last three diagonal entries of the balanced Gramians are relatively small. Eliminate these three least-contributing states with `xelim`

, using both matched DC gain and direct-deletion methods.

hmdc = xelim(hb,2:4,"MatchDC"); hdel = xelim(hb,2:4,"Truncate");

Both `hmdc`

and `hdel`

are first-order models. Compare their Bode responses against that of the original model.

op = bodeoptions; op.PhaseMatching = "on"; bodeplot(h,hmdc,'r--',hdel,'k-.',op) legend("Original","State elimination (match DC)",... "State elimination (truncate)")

The reduced-order model `hdel`

is clearly a better frequency-domain approximation of `h`

. Now compare the step responses.

stepplot(h,hmdc,'r--',hdel,'k-.') legend("Original","State elimination (match DC)",... "State elimination (truncate)",Location="southeast")

While `hdel`

accurately reflects the transient behavior, only `hmdc`

gives the true steady-state response.

For faster and more accurate results, use `reducespec`

for model reduction workflows.

### Find Matching Initial Condition for Reduced Model

*Since R2024a*

This example shows how use `findop`

to find the matching initial condition for a reduced model obtained with `xelim`

.

Create a random state-space model.

rng(0) sys = rss(10,1,2);

To reduce its order, first compute a balanced state-space realization with balreal.

[sysb,g] = balreal(sys); g'

`ans = `*1×10*
13.2301 4.9468 1.0334 0.4597 0.0644 0.0287 0.0057 0.0007 0.0000 0.0000

Based on the small Gramians, you can eliminate last 6 states from balanced realization `sysb`

.

`rsys = xelim(sysb,5:10,"MatchDC");`

With `MatchDC`

option, the reduced model states may differ from ${\mathit{x}}_{1}$ because `xelim`

scales and sometimes transforms the states $\mathit{x}$ to obtain the reduced model. Therefore, the initial conditions of the original model and reduced model may be different.

Compute the initial condition of the original and reduced models.

op = findop(sysb,u=[1 -1]); opr = findop(rsys,u=[1 -1]); op.x(1:4)

`ans = `*4×1*
18.9852
-3.2735
1.6693
0.5918

opr.x

`ans = `*4×1*
18.9852
-3.2735
0.8347
0.5918

The initial states for both models are different. Therefore, it is recommended to recompute the operating condition when reducing models. Using operating conditions of the original model with the reduced model may result in incorrect responses. For example, compare the initial response of the reduced model with operating condition from the original model.

t = 0:0.01:5; y = initial(sysb,op.x,t); y1 = initial(rsys,op.x(1:4),t); y2 = initial(rsys,opr.x,t); plot(t,y,t,y1,'k:',t,y2,'r--') legend("Original","Reduced (original IC)", "Reduced (recomputed IC)");

## Input Arguments

`sys`

— Dynamic system model

dynamic system model

Dynamic system model, specified as an ordinary or sparse LTI model.

The input model must have a valid state-space representation, such as
`tf`

, `ss`

, `sparss`

,
`mechss`

models. For generalized or uncertain state-space models
(`genss`

, `uss`

), the function uses the current value of
the model. For identified models (`idss`

), the function uses the
identified value.

`elim`

— State elimination vector

vector

State elimination vector, specified as one of these.

A vector containing index values of states you want to discard.

A vector of logical values of the same size as the number of states, where the

`true`

(`1`

) values specifies the states you want to discard.

`method`

— State elimination method

`"MatchDC"`

(default) | `"Truncate"`

State elimination method, specified as `"MatchDC"`

or
`"Truncate"`

. This argument specifies how the function eliminates the
states with weak contribution.

`"MatchDC"`

— Enforce matching DC gains. To do so, the algorithm treats*x*_{2}as infinitely fast and sets its derivative to zero. The resulting algebraic equation is used to express*x*_{2}in terms of*x*_{1}and eliminate it. For details, see Algorithms`"Truncate"`

— Simply drop*x*_{2}, and use*x*_{1}as reduced state.

The `"Truncate"`

option tends to produce a better approximation in
the frequency domain, but the DC gains are not guaranteed to match.

## Output Arguments

`rsys`

— Reduced-order model

state-space model

Reduced-order model, returned as a state-space model.

## Algorithms

For a state-space model

$$\begin{array}{l}\dot{x}=Ax+Bu\\ y=Cx+Du\end{array}$$

the function partitions the state vector into *x _{1}*
(to keep) and

*x*(to eliminate).

_{2}$$\begin{array}{l}\left[\begin{array}{c}{\dot{x}}_{1}\\ {\dot{x}}_{2}\end{array}\right]=\left[\begin{array}{cc}{A}_{11}& {A}_{12}\\ {A}_{21}& {A}_{22}\end{array}\right]\left[\begin{array}{c}{x}_{1}\\ {x}_{2}\end{array}\right]+\left[\begin{array}{c}{B}_{1}\\ {B}_{2}\end{array}\right]u\\ y=\left[\begin{array}{cc}{C}_{1}& {C}_{2}\end{array}\right]x+Du\end{array}$$

`"MatchDC"`

Method

For continuous-time models, this method sets the derivative of
*x _{2}* to zero and solves the resulting equation
for

*x*. The reduced-order model is given by

_{1}$$\begin{array}{l}{\dot{x}}_{1}=\left[{A}_{11}-{A}_{12}{A}_{22}{}^{-1}{A}_{21}\right]{x}_{1}+\left[{B}_{1}-{A}_{12}{A}_{22}{}^{-1}{B}_{2}\right]u\\ y=\left[{C}_{1}-{C}_{2}{A}_{22}{}^{-1}{A}_{21}\right]x+\left[D-{C}_{2}{A}_{22}{}^{-1}{B}_{2}\right]u\end{array}$$

Similarly, for discrete-time models, the algorithm sets $${x}_{2}[n+1]={x}_{2}[n]$$ to recompute the matrices.

These equations of the algorithm describe only the general idea. The function performs
additional scaling and eliminates only a portion of
*x _{2}* when

*A*or

_{22}*A*–

_{22}*E*is nearly singular. This alters

_{22}*x*and the original initial condition

_{1}*x*is not valid. You can use the

_{1}(0)`findop`

function to compute matching initial conditions for the reduced model. For an example, see
Find Matching Initial Condition for Reduced Model.`"Truncate"`

Method

For this method, the algorithm simply drops
*x*_{2}, and uses
*x*_{1} as reduced state. The reduced-order model is
given by

$$\begin{array}{l}{\dot{x}}_{1}={A}_{11}{x}_{1}+{B}_{1}u\\ y={C}_{1}{x}_{1}\end{array}$$

`xelim`

returns a scaled version of this realization.

## Version History

**Introduced in R2023b**

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