## Linearize Simulink Models

Generally, real systems are nonlinear. To design an MPC controller for a nonlinear system,
you can model the plant in Simulink^{®}.

Although an MPC controller can regulate a nonlinear plant, the model used within the controller must be linear. In other words, the controller employs a linear approximation of the nonlinear plant. The accuracy of this approximation significantly affects controller performance.

To obtain such a linear approximation, you *linearize* the nonlinear
plant at a specified *operating point*.

**Note**

The following examples require Simulink Control Design™ software.

You can linearize a Simulink model:

From the command line.

Using the Model Linearizer.

Using MPC Designer. For an example, see Linearize Simulink Models Using MPC Designer.

### Linearization Using MATLAB Code

This example shows how to obtain a linear model of a plant using a MATLAB script.

For this example the CSTR model, `CSTR_OpenLoop`

, is linearized. The model inputs are the coolant temperature (manipulated variable of the MPC controller), limiting reactant concentration in the feed stream, and feed temperature. The model states are the temperature and concentration of the limiting reactant in the product stream. Both states are measured and used for feedback control.

**Obtain Steady-State Operating Point**

The operating point defines the nominal conditions at which you linearize a model. It is usually a steady-state condition.

Suppose that you plan to operate the CSTR with the output concentration, `C_A`

, at $$2\phantom{\rule{0.2777777777777778em}{0ex}}kmol/{m}^{3}$$. The nominal feed concentration is $$10\phantom{\rule{0.2777777777777778em}{0ex}}kmol/{m}^{3}$$, and the nominal feed temperature is 300 K.

Create and visualize an operating point specification object to define the steady-state conditions.

opspec = operspec('CSTR_OpenLoop'); opspec = addoutputspec(opspec,'CSTR_OpenLoop/CSTR',2); opspec.Outputs(1).Known = true; opspec.Outputs(1).y = 2; opspec

opspec = Operating point specification for the Model CSTR_OpenLoop. (Time-Varying Components Evaluated at time t=0) States: ---------- x Known SteadyState Min Max dxMin dxMax ___________ ___________ ___________ ___________ ___________ ___________ ___________ (1.) CSTR_OpenLoop/CSTR/C_A 8.5695 false true 0 Inf -Inf Inf (2.) CSTR_OpenLoop/CSTR/T_K 311.267 false true 0 Inf -Inf Inf Inputs: ---------- u Known Min Max _____ _____ _____ _____ (1.) CSTR_OpenLoop/Coolant Temperature 0 false -Inf Inf Outputs: ---------- y Known Min Max _____ _____ _____ _____ (1.) CSTR_OpenLoop/CSTR 2 true -Inf Inf

Search for an operating point that satisfies the specifications.

`op1 = findop('CSTR_OpenLoop',opspec);`

Operating point search report: ---------------------------------

opreport = Operating point search report for the Model CSTR_OpenLoop. (Time-Varying Components Evaluated at time t=0) Operating point specifications were successfully met. States: ---------- Min x Max dxMin dx dxMax ___________ ___________ ___________ ___________ ___________ ___________ (1.) CSTR_OpenLoop/CSTR/C_A 0 2 Inf 0 -4.6683e-12 0 (2.) CSTR_OpenLoop/CSTR/T_K 0 373.1311 Inf 0 5.5678e-11 0 Inputs: ---------- Min u Max ________ ________ ________ (1.) CSTR_OpenLoop/Coolant Temperature -Inf 299.0349 Inf Outputs: ---------- Min y Max ___ ___ ___ (1.) CSTR_OpenLoop/CSTR 2 2 2

The calculated operating point is `C_A`

= $$2\phantom{\rule{0.2777777777777778em}{0ex}}kmol/{m}^{3}$$ and `T_K`

= 373 K. Notice that the steady-state coolant temperature is also given as 299 K, which is the nominal value of the input used to control the plant.

To specify:

Values of known inputs, use the

`Input.Known`

and`Input.u`

fields of`opspec`

Initial guesses for state values, use the

`State.x`

field of`opspec`

For example, the following code specifies the coolant temperature as 305 K and initial guess values of the `C_A`

and `T_K`

states before calculating the steady-state operating point:

opspec = operspec('CSTR_OpenLoop'); opspec.States(1).x = 1; opspec.States(2).x = 400; opspec.Inputs(1).Known = true; opspec.Inputs(1).u = 305; op2 = findop('CSTR_OpenLoop',opspec)

Operating point search report: ---------------------------------

opreport = Operating point search report for the Model CSTR_OpenLoop. (Time-Varying Components Evaluated at time t=0) Operating point specifications were successfully met. States: ---------- Min x Max dxMin dx dxMax ___________ ___________ ___________ ___________ ___________ ___________ (1.) CSTR_OpenLoop/CSTR/C_A 0 1.7787 Inf 0 0 0 (2.) CSTR_OpenLoop/CSTR/T_K 0 376.5371 Inf 0 -9.9476e-14 0 Inputs: ---------- Min u Max ___ ___ ___ (1.) CSTR_OpenLoop/Coolant Temperature 305 305 305 Outputs: None ----------

op2 = Operating point for the Model CSTR_OpenLoop. (Time-Varying Components Evaluated at time t=0) States: ---------- x ________ (1.) CSTR_OpenLoop/CSTR/C_A 1.7787 (2.) CSTR_OpenLoop/CSTR/T_K 376.5371 Inputs: ---------- u ___ (1.) CSTR_OpenLoop/Coolant Temperature 305

**Specify Linearization Inputs and Outputs**

If the linearization input and output signals are already defined in the model, as in `CSTR_OpenLoop`

, then use the following to obtain the signal set.

`io = getlinio('CSTR_OpenLoop');`

Otherwise, specify the input and output signals as shown here.

io(1) = linio('CSTR_OpenLoop/Coolant Temperature',1,'input'); io(2) = linio('CSTR_OpenLoop/Feed Concentration',1,'input'); io(3) = linio('CSTR_OpenLoop/Feed Temperature',1,'input'); io(4) = linio('CSTR_OpenLoop/CSTR',1,'output'); io(5) = linio('CSTR_OpenLoop/CSTR',2,'output');

**Linearize Model**

Linearize the model using the specified operating point, `op1`

, and input/output signals, `io`

.

`sys = linearize('CSTR_OpenLoop',op1,io)`

sys = A = C_A T_K C_A -5 -0.3427 T_K 47.68 2.785 B = Coolant Temp Feed Concent Feed Tempera C_A 0 1 0 T_K 0.3 0 1 C = C_A T_K CSTR/1 0 1 CSTR/2 1 0 D = Coolant Temp Feed Concent Feed Tempera CSTR/1 0 0 0 CSTR/2 0 0 0 Continuous-time state-space model.

Linearize the model also around the operating point, `op2`

, using the same input/output signals.

`sys = linearize('CSTR_OpenLoop',op2,io)`

sys = A = C_A T_K C_A -5.622 -0.3458 T_K 55.1 2.822 B = Coolant Temp Feed Concent Feed Tempera C_A 0 1 0 T_K 0.3 0 1 C = C_A T_K CSTR/1 0 1 CSTR/2 1 0 D = Coolant Temp Feed Concent Feed Tempera CSTR/1 0 0 0 CSTR/2 0 0 0 Continuous-time state-space model.

### Linearization Using Model Linearizer in Simulink Control Design

This example shows how to linearize a Simulink model using the Model Linearizer, provided by the Simulink Control Design software.

**Open Simulink Model**

This example uses the CSTR model, `CSTR_OpenLoop`

.

`open_system('CSTR_OpenLoop')`

**Specify Linearization Inputs and Outputs**

The linearization inputs and outputs are already specified for
`CSTR_OpenLoop`

. The input signals correspond to the outputs from the
`Feed Concentration`

, `Feed Temperature`

, and
`Coolant Temperature`

blocks. The output signals are the inputs to
the `CSTR Temperature`

and `Residual Concentration`

blocks.

To specify a signal as a linearization input or output, first in the Simulink
**Apps** tab, click **Linearization Manager**. Then,
in the Simulink model window, click the signal. Finally, in the **Insert Analysis
Points** gallery, in the **Closed Loop** section, select
either **Input Perturbation** for a linearization input or
**Output Measurement** for a linearization output.

**Open Model Linearizer**

To open the Model Linearizer, in the **Apps** tab,
click **Model Linearizer**.

**Specify Residual Concentration as Known Trim Constraint**

To specify the residual concentration as a known trim constant, first in the
Simulink
**Apps** tab, click **Linearization Manager**. Then,
in the Simulink model window, click the `CA`

output signal from the
`CSTR`

block. Finally, in the **Insert Analysis
Points** gallery, in the **Trim** section, select
**Trim Output Constraint**.

In the Model Linearizer, on the **Linear Analysis** tab,
select **Operating Point** > **Trim Model**.

In the Trim the model dialog box, on the **Outputs** tab:

Select the

**Known**check box for`Channel - 1`

under**CSTR_OpenLoop/CSTR**.Set the corresponding

**Value**to`2`

kmol/m^{3}.

**Create and Verify Operating Point**

In the Trim the model dialog box, click **Start
trimming**.

The Trim progress viewer window opens up showing the optimization progress towards
finding a point in the state-input space of the model with the characteristics specified
in the **States**, **Inputs**, and
**Outputs** tabs. After the optimization process terminates, close
the trim progress window as well as the Trim the model dialog box.

The operating point `op_trim1`

displays in the **Linear
Analysis Workspace** of Model Linearizer. Select
`op_trim1`

to display basic information in the **Linear
Analysis Workspace** section.

Double click `op_trim1`

to view the resulting operating point in
the Edit dialog box.

In the Edit dialog box, select the **Input** tab.

The coolant temperature at steady state is 299 K, as desired. Close the Edit dialog box.

**Linearize Model**

On the **Linear Analysis** tab, in the **Operating
Point** drop-down list, make sure `op_trim1`

is
selected.

In the **Linearize** section, click **Step** to linearize the Simulink model and display the step response of the linearized model.

This option creates the linear model `linsys1`

in the
**Linear Analysis Workspace** and generates a step response for this
model. `linsys1`

uses `op_trim1`

as its operating
point.

The step response from feed concentration to output `CSTR/2`

displays an interesting inverse response. An examination of the linear model shows that
`CSTR/2`

is the residual CSTR concentration, `C_A`

.
When the feed concentration increases, `C_A`

increases initially
because more reactant is entering, which increases the reaction rate. This rate increase
results in a higher reactor temperature (output `CSTR/1`

), which
further increases the reaction rate and `C_A`

decreases
dramatically.

**Export Linearization Result**

If necessary, you can repeat any of these steps to improve your model performance.
Once you are satisfied with your linearization result, in the Model
Linearizer, drag the linear model from the **Linear Analysis
Workspace** section of **Model Linearizer** to the
**MATLAB Workspace** section just above it. You can now use your
linear model to design an MPC controller.

## See Also

### Apps

- Model Linearizer (Simulink Control Design) | MPC Designer

### Functions

`linearize`

(Simulink Control Design)

### Objects

## Related Examples

- Design MPC Controller in Simulink
- Design Controller Using MPC Designer
- Design MPC Controller at the Command Line
- Linearize Simulink Models Using MPC Designer