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.
Note: 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 the MPC Designer app.
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 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 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/Feed Concentration', 1, 'input'); io(2) = linio('CSTR_OpenLoop/Feed Temperature', 1, 'input'); io(3) = linio('CSTR_OpenLoop/Coolant 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 = Feed Concent Feed Tempera Coolant Temp C_A 1 0 0 T_K 0 1 0.3 c = C_A T_K CSTR/1 0 1 CSTR/2 1 0 d = Feed Concent Feed Tempera Coolant Temp 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.