# Modeling a Family of Responses as an Uncertain System

This example shows how to use the Robust Control Toolbox™ command `ucover`

to model a family of LTI responses as an uncertain system. This command is useful to fit an uncertain model to a set of frequency responses representative of the system variability, or to reduce the complexity of an existing uncertain model to facilitate the synthesis of robust controllers with `musyn`

.

### Modeling Plant Variability as Uncertainty

In this first example, we have a family of models describing the plant behavior under various operating conditions. The nominal plant model is a first-order unstable system.

Pnom = tf(2,[1 -2])

Pnom = 2 ----- s - 2 Continuous-time transfer function.

The other models are variations of `Pnom`

. They all have a single unstable pole but the location of this pole may vary with the operating condition.

p1 = Pnom*tf(1,[.06 1]); % extra lag p2 = Pnom*tf([-.02 1],[.02 1]); % time delay p3 = Pnom*tf(50^2,[1 2*.1*50 50^2]); % high frequency resonance p4 = Pnom*tf(70^2,[1 2*.2*70 70^2]); % high frequency resonance p5 = tf(2.4,[1 -2.2]); % pole/gain migration p6 = tf(1.6,[1 -1.8]); % pole/gain migration

To apply robust control tools, we can replace this set of models with a single uncertain plant model whose range of behaviors includes `p1`

through `p6`

. This is one use of the command `ucover`

. This command takes an array of LTI models `Parray`

and a nominal model `Pnom`

and models the difference `Parray-Pnom`

as multiplicative uncertainty in the system dynamics.

Because `ucover`

expects an array of models, use the `stack`

command to gather the plant models `p1`

through `p6`

into one array.

Parray = stack(1,p1,p2,p3,p4,p5,p6);

Next, use `ucover`

to "cover" the range of behaviors `Parray`

with an uncertain model of the form

P = Pnom * (1 + Wt * Delta)

where all uncertainty is concentrated in the "unmodeled dynamics" `Delta`

(a `ultidyn`

object). Because the gain of `Delta`

is uniformly bounded by 1 at all frequencies, a "shaping" filter `Wt`

is used to capture how the relative amount of uncertainty varies with frequency. This filter is also referred to as the uncertainty weighting function.

Try a 4th-order filter `Wt`

for this example:

```
orderWt = 4;
Parrayg = frd(Parray,logspace(-1,3,60));
[P,Info] = ucover(Parrayg,Pnom,orderWt,'InputMult');
```

The resulting model `P`

is a single-input, single-output uncertain state-space (USS) object with nominal value `Pnom`

.

P

Uncertain continuous-time state-space model with 1 outputs, 1 inputs, 5 states. The model uncertainty consists of the following blocks: Parrayg_InputMultDelta: Uncertain 1x1 LTI, peak gain = 1, 1 occurrences Type "P.NominalValue" to see the nominal value and "P.Uncertainty" to interact with the uncertain elements.

tf(P.NominalValue)

ans = 2 ----- s - 2 Continuous-time transfer function.

A Bode magnitude plot confirms that the shaping filter `Wt`

"covers" the relative variation in plant behavior. As a function of frequency, the uncertainty level is 30% at 5 rad/sec (-10dB = 0.3) , 50% at 10 rad/sec, and 100% beyond 29 rad/sec.

Wt = Info.W1; bodemag((Pnom-Parray)/Pnom,'b--',Wt,'r'); grid title('Relative Gaps vs. Magnitude of Wt')

You can now use the uncertain model `P`

to design a robust controller for the original family of plant models, see Simultaneous Stabilization Using Robust Control for details.

### Simplifying an Existing Uncertain Model

In this second example, we start with a detailed uncertain model of the plant. This model consists of first-order dynamics with uncertain gain and time constant, in series with a mildly underdamped resonance and significant unmodeled dynamics. This model is created using the `ureal`

and `ultidyn`

commands for specifying uncertain variables:

gamma = ureal('gamma',2,'Perc',30); % uncertain gain tau = ureal('tau',1,'Perc',30); % uncertain time-constant wn = 50; xi = 0.25; P = tf(gamma,[tau 1]) * tf(wn^2,[1 2*xi*wn wn^2]); % Add unmodeled dynamics and set SampleStateDim to 5 to get representative % sample values of the uncertain model P delta = ultidyn('delta',[1 1],'SampleStateDim',5,'Bound',1); W = makeweight(0.1,20,10); P = P * (1+W*delta)

Uncertain continuous-time state-space model with 1 outputs, 1 inputs, 4 states. The model uncertainty consists of the following blocks: delta: Uncertain 1x1 LTI, peak gain = 1, 1 occurrences gamma: Uncertain real, nominal = 2, variability = [-30,30]%, 1 occurrences tau: Uncertain real, nominal = 1, variability = [-30,30]%, 1 occurrences Type "P.NominalValue" to see the nominal value and "P.Uncertainty" to interact with the uncertain elements.

A collection of step responses illustrates the plant variability.

```
step(P,4)
title('Sampled Step Responses of Uncertain System')
```

The uncertain plant model `P`

contains 3 uncertain elements. For control design purposes, it is often desirable to simplify this uncertainty model while approximately retaining its overall variability. This is another use of the command `ucover`

.

To use `ucover`

in this context, first map the uncertain model `P`

into an array of LTI models using `usample`

. This command samples the uncertain elements in an uncertain system and returns the corresponding LTI models, each model representing one possible behavior of the uncertain system. In this example, sample `P`

at 60 points (the random number generator is seeded for repeatability):

```
rng(0,'twister');
Parray = usample(P,60);
```

Next, use `ucover`

to cover all behaviors in `Parray`

by a simple uncertainty model `Usys`

. Choose the nominal value of `P`

as center of the cover, and use a 2nd-order filter to model the frequency distribution of the unmodeled dynamics.

```
orderWt = 2;
Parrayg = frd(Parray,logspace(-3,3,60));
[Usys,Info] = ucover(Parrayg,P.NominalValue,orderWt,'InputMult');
```

A Bode magnitude plot shows how the filter magnitude (in red) "covers" the relative variability of the plant frequency response (in blue).

Wt = Info.W1; bodemag((P.NominalValue-Parray)/P.NominalValue,'b--',Wt,'r') title('Relative Gaps (blue) vs. Shaping Filter Magnitude (red)')

You can now use the simplified uncertainty model `Usys`

to design a robust controller for the original plant, see First-Cut Robust Design for details.

### Adjusting the Uncertainty Weighting

In this third example, we start with 40 frequency responses of a 2-input, 2-output system. This data has been collected with a frequency analyzer under various operating conditions. A two-state nominal model is fitted to the most typical response:

A = [-5 10;-10 -5]; B = [1 0;0 1]; C = [1 10;-10 1]; D = 0; Pnom = ss(A,B,C,D);

The frequency response data is loaded into a 40-by-1 array of FRD models:

```
load ucover_demo
size(Pdata)
```

40x1 array of FRD models. Each model has 2 outputs, 2 inputs, and 120 frequency points.

Plot this data and superimpose the nominal model.

bode(Pdata,'b--',Pnom,'r',{.1,1e3}), grid legend('Frequency response data','Nominal model','Location','NorthEast')

Because the response variability is modest, try modeling this family of frequency responses using an additive uncertainty model of the form

P = Pnom + w * Delta

where `Delta`

is a 2-by-2 `ultidyn`

object representing the unmodeled dynamics and `w`

is a scalar weighting function reflecting the frequency distribution of the uncertainty (variability in Pdata).

Start with a first-order filter `w`

and compare the magnitude of `w`

with the minimum amount of uncertainty needed at each frequency:

[P1,InfoS1] = ucover(Pdata,Pnom,1,'Additive'); w = InfoS1.W1; bodemag(w,'r',InfoS1.W1opt,'g',{1e-1 1e3}) title('Scalar Additive Uncertainty Model') legend('First-order w','Min. uncertainty amount','Location','SouthWest')

The magnitude of `w`

should closely match the minimum uncertainty amount. It is clear that the first-order fit is too conservative and exceeds this minimum amount at most frequencies. Try again with a third-order filter `w`

. For speed, reuse the data in `InfoS1`

to avoid recomputing the optimal uncertainty scaling at each frequency.

[P3,InfoS3] = ucover(Pnom,InfoS1,3,'Additive'); w = InfoS3.W1; bodemag(w,'r',InfoS3.W1opt,'g',{1e-1 1e3}) title('Scalar Additive Uncertainty Model') legend('Third-order w','Min. uncertainty amount','Location','SouthWest')

The magnitude of `w`

now closely matches the minimum uncertainty amount. Among additive uncertainty models, `P3`

provides a tight cover of the behaviors in `Pdata`

. Note that `P3`

has a total of 8 states (2 from the nominal part and 6 from `w`

).

P3

Uncertain continuous-time state-space model with 2 outputs, 2 inputs, 8 states. The model uncertainty consists of the following blocks: Pdata_AddDelta: Uncertain 2x2 LTI, peak gain = 1, 1 occurrences Type "P3.NominalValue" to see the nominal value and "P3.Uncertainty" to interact with the uncertain elements.

You can refine this additive uncertainty model by using non-scalar uncertainty weighting functions, for example

P = Pnom + W1*Delta*W2

where `W1`

and `W2`

are 2-by-2 diagonal filters. In this example, restrict use `W2=1`

and allow both diagonal entries of W1 to be third order.

```
[PM,InfoM] = ucover(Pdata,Pnom,[3;3],[],'Additive');
```

Compare the two entries of `W1`

with the minimum uncertainty amount computed earlier. Note that at all frequencies, one of the diagonal entries of `W1`

has magnitude much smaller than the scalar filter `w`

. This suggests that the diagonally-weighted uncertainty model yields a less conservative cover of the frequency response family.

bodemag(InfoS1.W1opt,'g*',... InfoM.W1opt(1,1),'r--',InfoM.W1(1,1),'r',... InfoM.W1opt(2,2),'b--',InfoM.W1(2,2),'b',{1e-1 1e3}); title('Diagonal Additive Uncertainty Model') legend('Scalar Optimal Weight',... 'W1(1,1), pointwise optimal',... 'W1(1,1), 3rd-order fit',... 'W1(2,2), pointwise optimal',... 'W1(2,2), 3rd-order fit',... 'Location','SouthWest')

The degree of conservativeness of one cover over another can be partially quantified by considering the two frequency-dependent quantities:

Fd2s = norm(inv(W1)*w) , Fs2d = norm(W1/w)

These quantities measure by how much one uncertainty model needs to be scaled to cover the other. For example, the uncertainty model `Pnom + W1*Delta`

needs to be enlarged by a factor `Fd2s`

to include all of the models represented by the uncertain model `Pnom + w*Delta`

.

Plot `Fd2s`

and `Fs2d`

as a function of frequency.

Fd2s = fnorm(InfoS1.W1opt*inv(InfoM.W1opt)); Fs2d = fnorm(InfoM.W1opt*inv(InfoS1.W1opt)); semilogx(fnorm(Fd2s),'b',fnorm(Fs2d),'r'), grid axis([0.1 1000 0.5 2.6]) xlabel('Frequency (rad/s)'), ylabel('Magnitude') title('Scale factors relating different covers') legend('Diagonal to Scalar factor',... 'Scalar to Diagonal factor','Location','SouthWest');

This plot shows that:

`Fs2d = 1`

in a large frequency range so`Pnom+w*Delta`

includes all the behaviors modeled by`Pnom+W1*Delta`

In that same frequency range,

`Pnom+W1*Delta`

does not include all of the behaviors modeled by`Pnom+w*Delta`

and, in fact, would need to be enlarged by a factor between 1.2 and 2.6 in order to do so.

In the frequency range [1 20], neither uncertainty model contains the other, but at all frequencies, making

`Pnom+W1*Delta`

cover`Pnom+w*Delta`

requires a much smaller scaling factor than the converse.

This indicates that the `Pnom+W1*Delta`

model provides a less conservative cover of the frequency response data in `Pdata`

.