Note: This page has been translated by MathWorks. Please click here

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

To view all translated materals including this page, select Japan from the country navigator on the bottom of this page.

**Products Used**- Simulink
^{®}Control Design™

Generally, real systems are nonlinear. To design an MPC controller for a nonlinear system, you must 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*.

Simulink Control Design software must be installed to linearize nonlinear Simulink models.

You can linearize a Simulink model:

From the command line.

Using the Linear Analysis Tool.

Using

**MPC Designer**.

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 . The nominal feed concentration is , and the nominal feed temperature is 300 K. Create 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; op1 = findop('CSTR_OpenLoop',opspec);

Operating point search report: --------------------------------- Operating point search report for the Model CSTR_OpenLoop. (Time-Varying Components Evaluated at time t=0) Operating point specifications were successfully met. States: ---------- (1.) CSTR_OpenLoop/CSTR/C_A x: 2 dx: -4.6e-12 (0) (2.) CSTR_OpenLoop/CSTR/T_K x: 373 dx: 5.49e-11 (0) Inputs: ---------- (1.) CSTR_OpenLoop/Coolant Temperature u: 299 [-Inf Inf] Outputs: ---------- (1.) CSTR_OpenLoop/CSTR y: 2 (2)

The calculated operating point is `C_A`

= 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 manipulated variable of the MPC controller.

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: --------------------------------- Operating point search report for the Model CSTR_OpenLoop. (Time-Varying Components Evaluated at time t=0) Operating point specifications were successfully met. States: ---------- (1.) CSTR_OpenLoop/CSTR/C_A x: 1.78 dx: -8.88e-15 (0) (2.) CSTR_OpenLoop/CSTR/T_K x: 377 dx: 1.14e-13 (0) Inputs: ---------- (1.) CSTR_OpenLoop/Coolant Temperature u: 305 Outputs: None ----------

**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.

This example shows how to linearize a Simulink model using the Linear Analysis Tool, provided by the Simulink Control Design product.

This example uses the CSTR model, `CSTR_OpenLoop`

.

**Open Simulink Model**

```
sys = 'CSTR_OpenLoop';
open_system(sys)
```

**Open Linear Analysis Tool**

In the Simulink model window, select **Analysis** > **Control Design** > **Linear Analysis**.

**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, right-click the signal in the Simulink model window and select

**Linear Analysis Points**>**Input Perturbation**.Linearization output, right-click the signal in the Simulink model window and select

**Linear Analysis Points**>**Output Measurement**.

**Specify Residual Concentration as Known Trim Constraint**

In the Simulink model window, right-click the `CA`

output
signal from the `CSTR`

block. Select **Linear Analysis Points** > **Trim
Output Constraint**.

In the Linear Analysis Tool, in the **Linear Analysis** tab,
in the **Operating Point** drop-down list, select ```
Trim
model
```

.

In 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 operating point `op_trim1`

displays in
the **Linear Analysis Workspace**.

Double click `op_trim1`

to view the resulting
operating point.

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

The coolant temperature at steady state is 299 K, as desired.

**Linearize Model**

In the **Linear Analysis** tab, in the **Operating
Point** drop-down list, select `op_trim1`

.

Click **Step** to linearize
the model.

This option creates the linear model `linsys1`

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

uses `optrim1`

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 Linear Analysis Tool, drag and drop it from the **Linear
Analysis Workspace** to the **MATLAB Workspace**.
You can now use your linear model to design an MPC controller.

Linear Analysis Tool | `linearize`

Was this topic helpful?