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Model order reduction

`[`

computes a reduced-order approximation `rsys`

,`info`

] = balred(`sys`

,`order`

)`rsys`

of the LTI model
`sys`

. The desired order (number of states) is specified by
`order`

. You can try multiple orders at once by setting
`order`

to a vector of integers, in which case
`rsys`

is an array of reduced models. `balred`

also
returns a structure `info`

with additional information like the Hankel
singular values (HSV), error bound, regularization level and the Cholesky factors of the
gramians.

`[~,`

returns the structure `info`

] = balred(`sys`

)`info`

without computing the reduced-order model.
You can use this information to select the reduced order `order`

based
on your desired fidelity.

**Note**

When performance is a concern, avoid computing the Hankel singular values twice by
using the information obtained from the above syntax to select the desired model order
and then use `rsys = balred(sys,order,info)`

to compute the
reduced-order model.

`[___] = balred(___,`

computes the reduced model using the options set `opts`

)`opts`

that you
specify using `balredOptions`

(Control System Toolbox). You can specify additional
options for eliminating states, using absolute vs. relative error control, emphasizing
certain time or frequency bands, and separating the stable and unstable modes. See
`balredOptions`

(Control System Toolbox) to create and configure the
option set `opts`

.

`balred`

first decomposes*G*into its stable and unstable parts:$$G={G}_{s}+{G}_{u}$$

When you specify

`ErrorBound`

as`absolute`

,`balred`

uses the balanced truncation method of [1] to reduce*G*. This computes the Hankel singular values (HSV)_{s}*σ*based on the controllability and observability gramians. For order_{j}*r*, the absolute error $${\Vert {G}_{s}-{G}_{r}\Vert}_{\infty}$$ is bounded by $$2{\displaystyle \sum _{j=r+1}^{n}{\sigma}_{j}}$$. Here,*n*is the number of states in*G*._{s}When you specify

`ErrorBound`

as`relative`

,`balred`

uses the balanced stochastic truncation method of [2] to reduce*G*. For square_{s}*G*, this computes the HSV_{s}*σ*of the phase matrix $$F={\left(W\text{'}\right)}^{-1}G$$ where_{j}*W(s)*is a stable, minimum-phase spectral factor of*GG’*:$$W\text{'}(s)W(s)=G(s)G\text{'}(s)$$

For order

*r*, the relative error $${\Vert {G}_{s}{}^{-1}({G}_{s}-{G}_{r})\Vert}_{\infty}$$ is bounded by:$$\prod _{j=r+1}^{H}\left(\frac{1+{\sigma}_{j}}{1-{\sigma}_{j}}\right)}-1\approx 2{\displaystyle \sum _{j=r+1}^{n}{\sigma}_{j}$$

when, $$2{\displaystyle \sum _{j=r+1}^{n}{\sigma}_{j}}\ll 1$$.

**Model Reducer (Control System Toolbox)**

Reduce Model Order (Control System Toolbox)

[1] Varga, A., "Balancing-Free
Square-Root Algorithm for Computing Singular Perturbation Approximations," *Proc. of
30th IEEE CDC*, Brighton, UK (1991), pp. 1062-1065.

[2] Green, M., "A Relative Error Bound
for Balanced Stochastic Truncation", *IEEE Transactions on Automatic
Control*, Vol. 33, No. 10, 1988

`balredOptions`

(Control System Toolbox)

- Model Reducer (Control System Toolbox)

- Reduce Model Order (Control System Toolbox)

- Model Reduction Basics (Control System Toolbox)
- Balanced Truncation Model Reduction (Control System Toolbox)