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This example shows how to tune the following control system to achieve a loop crossover frequency between 0.1 and 1 rad/s, using `looptune`

.

The plant, `G`

, is a two-input, two-output model (*y* is a two-element vector signal). For this example, the transfer function of `G`

is given by:

$$G\left(s\right)=\frac{1}{75s+1}\left[\begin{array}{cc}87.8& -86.4\\ 108.2& -109.6\end{array}\right].$$

This sample plant is based on the distillation column described in more detail in the example Decoupling Controller for a Distillation Column.

To tune this control system, you first create a numeric model of the plant. Then you create tunable models of the controller elements and interconnect them to build a controller model. Then you use `looptune`

to tune the free parameters of the controller model. Finally, examine the performance of the tuned system to confirm that the tuned controller yields desirable performance.

Create a model of the plant.

s = tf('s'); G = 1/(75*s+1)*[87.8 -86.4; 108.2 -109.6]; G.InputName = {'qL','qV'}; G.OutputName = 'y';

When you tune the control system, `looptune`

uses the channel names `G.InputName`

and `G.OutputName`

to interconnect the plant and controller. Therefore, assign these channel names to match the illustration. When you set `G.OutputName = 'y'`

, the `G.OutputName`

is automatically expanded to `{'y(1)';'y(2)'}`

. This expansion occurs because `G`

is a two-output system.

Represent the components of the controller.

D = tunableGain('Decoupler',eye(2)); D.InputName = 'e'; D.OutputName = {'pL','pV'}; PI_L = tunablePID('PI_L','pi'); PI_L.InputName = 'pL'; PI_L.OutputName = 'qL'; PI_V = tunablePID('PI_V','pi'); PI_V.InputName = 'pV'; PI_V.OutputName = 'qV'; sum1 = sumblk('e = r - y',2);

The control system includes several tunable control elements. `PI_L`

and `PI_V`

are tunable PI controllers. These elements represented by `tunablePID`

models. The fixed control structure also includes a decoupling gain matrix `D`

, represented by a tunable `tunableGain`

model. When the control system is tuned, `D`

ensures that each output of `G`

tracks the corresponding reference signal `r`

with minimal crosstalk.

Assigning `InputName`

and `OutputName`

values to these control elements allows you to interconnect them to create a tunable model of the entire controller `C`

as shown.

When you tune the control system, `looptune`

uses these channel names to interconnect `C`

and `G`

. The controller `C`

also includes the summing junction `sum1`

. This a two-channel summing junction, because `r`

and `y`

are vector-valued signals of dimension 2.

Connect the controller components.

C0 = connect(PI_L,PI_V,D,sum1,{'r','y'},{'qL','qV'});

`C0`

is a tunable `genss`

model that represents the entire controller structure. `C0`

stores the tunable controller parameters and contains the initial values of those parameters.

Tune the control system.

The inputs to `looptune`

are `G`

and `C0`

, the plant and initial controller models that you created. The input `wc = [0.1,1]`

sets the target range for the loop bandwidth. This input specifies that the crossover frequency of each loop in the tuned system fall between 0.1 and 1 rad/min.

wc = [0.1,1]; [G,C,gam,Info] = looptune(G,C0,wc);

The displayed `Peak Gain = 0.949`

indicates that `looptune`

has found parameter values that achieve the target loop bandwidth. `looptune`

displays the final peak gain value of the optimization run, which is also the output `gam`

. If `gam`

is less than 1, all tuning requirements are satisfied. A value greater than 1 indicates failure to meet some requirement. If `gam`

exceeds 1, you can increase the target bandwidth range or relax another tuning requirement.

`looptune`

also returns the tuned controller model `C`

. This model is the tuned version of `C0`

. It contains the PI coefficients and the decoupling matrix gain values that yield the optimized peak gain value.

Display the tuned controller parameters.

showTunable(C)

Check the time-domain response for the control system with the tuned coefficients. To produce a plot, construct a closed-loop model of the tuned control system. Plot the step response from reference to output.

T = connect(G,C,'r','y'); step(T)

The decoupling matrix in the controller permits each channel of the two-channel output signal `y`

to track the corresponding channel of the reference signal `r`

, with minimal crosstalk. From the plot, you can how well this requirement is achieved when you tune the control system for bandwidth alone. If the crosstalk still exceeds your design requirements, you can use a `TuningGoal.Gain`

requirement object to impose further restrictions on tuning.

Examine the frequency-domain response of the tuned result as an alternative method for validating the tuned controller.

```
figure('Position',[100,100,520,1000])
loopview(G,C,Info)
```

The first plot shows that the open-loop gain crossovers fall within the specified interval `[0.1,1]`

. This plot also includes the maximum and tuned values of the sensitivity function $$S=(I-GC{)}^{-1}$$ and complementary sensitivity $$T=I-S$$. The second and third plots show that the MIMO stability margins of the tuned system (blue curve) do not exceed the upper limit (yellow curve).