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*Linearization* is a linear approximation
of a nonlinear system that is valid in a small region around an operating
point.

For example, suppose that the nonlinear function is $$y={x}^{2}$$. Linearizing this nonlinear
function about the operating point * x =
1*,

Near the operating point, $$y=2x-1$$ is a good approximation to $$y={x}^{2}$$. Away from the operating point, the approximation is poor.

The next figure shows a possible region of good approximation for the linearization of $$y={x}^{2}$$. The actual region of validity depends on the nonlinear model.

Extending the concept of linearization to dynamic systems, you can write continuous-time nonlinear differential equations in this form:

$$\begin{array}{l}\dot{x}(t)=f\left(x(t),u(t),t\right)\\ y(t)=g\left(x(t),u(t),t\right).\end{array}$$

In these equations, *x*(*t*)
represents the system states, *u*(*t*)
represents the inputs to the system, and *y*(*t*)
represents the outputs of the system.

A linearized model of this system is valid in a small region
around the operating point *t*=*t*_{0}, *x*(*t*_{0})=*x*_{0}, *u*(*t*_{0})=*u*_{0},
and *y*(*t*_{0})=*g*(*x*_{0},*u*_{0},*t*_{0})=*y*_{0}.

To represent the linearized model, define new variables centered about the operating point:

$$\begin{array}{l}\delta x(t)=x(t)-{x}_{0}\\ \delta u(t)=u(t)-{u}_{0}\\ \delta y(t)=y(t)-{y}_{0}\end{array}$$

The linearized model in terms of δ*x*,
δ*u*, and δ*y* is
valid when the values of these variables are small:

$$\begin{array}{l}\delta \dot{x}(t)=A\delta x(t)+B\delta u(t)\\ \delta y(t)=C\delta x(t)+D\delta u(t)\end{array}$$

Linearization is useful in model analysis and control design applications.

Exact linearization of the specified nonlinear Simulink^{®} model
produces linear state-space, transfer-function, or zero-pole-gain
equations that you can use to:

Plot the Bode response of the Simulink model.

Evaluate loop stability margins by computing open-loop response.

Analyze and compare plant response near different operating points.

Design linear controller

Classical control system analysis and design methodologies require linear, time-invariant models. Simulink Control Design™ automatically linearizes the plant when you tune your compensator. See Choosing a Control Design Approach.

Analyze closed-loop stability.

Measure the size of resonances in frequency response by computing closed-loop linear model for control system.

Generate controllers with reduced sensitivity to parameter variations and modeling errors.

You can use Simulink Control Design to linearize continuous-time, discrete-time, or multirate Simulink models. The resulting linear time-invariant model is in state-space form.

Simulink Control Design uses a *block-by-block* approach
to linearize models, instead of using *full-model perturbation*.
This block-by-block approach individually linearizes each block in
your Simulink model and combines the results to produce the linearization
of the specified system.

The block-by-block linearization approach has several advantages to full-model numerical perturbation:

Most Simulink blocks have preprogrammed linearization that provides Simulink Control Design an exact linearization of each block at the operating point.

You can configure blocks to use custom linearizations without affecting your model simulation.

Simulink Control Design automatically removes nonminimal states.

Ability to specify linearization to be uncertain (requires Robust Control Toolbox™)

Exact linearization supports most Simulink blocks.

However, Simulink blocks with strong discontinuities or event-based dynamics linearize (correctly) to zero or large (infinite) gain. Sources of event-based or discontinuous behavior exist in models that have Simulink Control Design requires special handling of models that include:

Blocks from Discontinuities library

Stateflow

^{®}chartsTriggered subsystems

Pulse width modulation (PWM) signals

For most applications, the states in your Simulink model should be at steady state. Otherwise, your linear model is only valid over a small time interval.

Choosing the right operating point for linearization is critical for obtaining an accurate linear model. The linear model is an approximation of the nonlinear model that is valid only near the operating point at which you linearize the model.

Although you specify which Simulink blocks to linearize, all blocks in the model affect the operating point.

A nonlinear model can have two very different linear approximations when you linearize about different operating points.

The linearization result for this model is shown next, with
the initial condition for the integration *x*_{0} =
0.

This table summarizes the different linearization results for two different operating points.

Operating Point | Linearization Result |
---|---|

Initial Condition = 5, State `x1` = 5 | 30/s |

Initial Condition = 0, State `x1` = 0 | 0 |

You can linearize your Simulink model at three different types of operating points:

Trimmed operating point — Linearize at Trimmed Operating Point

Simulation snapshot — Linearize at Simulation Snapshot

Triggered simulation event — Linearize at Triggered Simulation Events

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