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*.

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

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.

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 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`

= $$2\phantom{\rule{0.2777777777777778em}{0ex}}kmol/{m}^{3}$$ 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 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: -1.42e-14 (0) (2.) CSTR_OpenLoop/CSTR/T_K x: 377 dx: 5.68e-14 (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 **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 open the
**Linearization** tab. To do so, in the Simulink
**Apps** gallery, click **Linearization Manager**.
Then, in the Simulink model window, click the signal.

To specify the signal as a:

Linearization input, on the

**Linearization**tab, in the**Insert Analysis Points**gallery, click**Input Perturbation**.Linearization output, on the

**Linearization**tab, in the**Insert Analysis Points**gallery, click**Output Measurement**.

**Open Model Linearizer**

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

**Specify Residual Concentration as Known Trim Constraint**

In the Simulink model window, click the `CA`

output signal from the
`CSTR`

block. Then, on the **Linearization** tab, in
the **Insert Analysis Points** gallery, click **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 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**

On 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 **Model
Linearizer**, 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.