# Realization (systems)

Realization, in the system theory context refers to a state space model implementing a given input-output behavior. That is, given an input-output relationship, a realization is a quadruple of (time-varying) matrices $[A(t),B(t),C(t),D(t)]$ such that

${\dot {\mathbf {x} }}(t)=A(t)\mathbf {x} (t)+B(t)\mathbf {u} (t)$ $\mathbf {y} (t)=C(t)\mathbf {x} (t)+D(t)\mathbf {u} (t)$ ## LTI System

### Canonical realizations

Any given transfer function which is strictly proper can easily be transferred into state-space by the following approach (this example is for a 4-dimensional, single-input, single-output system)):

Given a transfer function, expand it to reveal all coefficients in both the numerator and denominator. This should result in the following form:

$H(s)={\frac {n_{1}s^{3}+n_{2}s^{2}+n_{3}s+n_{4}}{s^{4}+d_{1}s^{3}+d_{2}s^{2}+d_{3}s+d_{4}}}$ .

The coefficients can now be inserted directly into the state-space model by the following approach:

${\dot {\textbf {x}}}(t)={\begin{bmatrix}-d_{1}&-d_{2}&-d_{3}&-d_{4}\\1&0&0&0\\0&1&0&0\\0&0&1&0\end{bmatrix}}{\textbf {x}}(t)+{\begin{bmatrix}1\\0\\0\\0\\\end{bmatrix}}{\textbf {u}}(t)$ ${\textbf {y}}(t)={\begin{bmatrix}n_{1}&n_{2}&n_{3}&n_{4}\end{bmatrix}}{\textbf {x}}(t)$ .

This state-space realization is called controllable canonical form (also known as phase variable canonical form) because the resulting model is guaranteed to be controllable (i.e., because the control enters a chain of integrators, it has the ability to move every state).

The transfer function coefficients can also be used to construct another type of canonical form

${\dot {\textbf {x}}}(t)={\begin{bmatrix}-d_{1}&1&0&0\\-d_{2}&0&1&0\\-d_{3}&0&0&1\\-d_{4}&0&0&0\end{bmatrix}}{\textbf {x}}(t)+{\begin{bmatrix}n_{1}\\n_{2}\\n_{3}\\n_{4}\end{bmatrix}}{\textbf {u}}(t)$ ${\textbf {y}}(t)={\begin{bmatrix}1&0&0&0\end{bmatrix}}{\textbf {x}}(t)$ .

This state-space realization is called observable canonical form because the resulting model is guaranteed to be observable (i.e., because the output exits from a chain of integrators, every state has an effect on the output).

## System identification

{{#invoke:main|main}} System identification techniques take the experimental data from a system and output a realization. Such techniques can utilize both input and output data (e.g. eigensystem realization algorithm) or can only include the output data (e.g. frequency domain decomposition). Typically an input-output technique would be more accurate, but the input data is not always available.