This example uses the Robust Control Toolbox™ commands `ucover`

and `dksyn`

to design a high-performance controller for a family of unstable plants.

The nominal plant model consists of a first-order unstable system.

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

The family of perturbed plants are variations of `Pnom`

. All plants have a single unstable pole but the location of this pole varies across the family.

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

For feedback design purposes, we need to 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

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, "get(P)" to see all properties, 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')

To design a robust controller for the uncertain plant model `P`

, we choose a desired closed-loop bandwidth and minimize the sensitivity to disturbances at the plant output. The control structure is shown below. The signals `d`

and `n`

are the load disturbance and measurement noise. The controller uses a noisy measurement of the plant output `y`

to generate the control signal `u`

.

**Figure 1**: Control Structure.

The filters `Wperf`

and `Wnoise`

are selected to enforce the desired bandwidth and some adequate roll-off. The closed-loop transfer function from `[d;n]`

to `y`

is

y = [Wperf * S , Wnoise * T] [d;n]

where `S=1/(1+PC)`

and `T=PC/(1+PC)`

are the sensitivity and complementary sensitivity functions. If we design a controller that keeps the closed-loop gain from `[d;n]`

to `y`

below 1, then

|S| < 1/|Wperf| , |T| < 1/|Wnoise|

By choosing appropriate magnitude profiles for `Wperf`

and `Wnoise`

, we can enforce small sensitivity (`S`

) inside the bandwidth and adequate roll-off (`T`

) outside the bandwidth.

For example, choose `Wperf`

as a first-order low-pass filter with a DC gain of 500 and a gain crossover at the desired bandwidth `desBW`

:

desBW = 4.5; Wperf = makeweight(500,desBW,0.33); tf(Wperf)

ans = 0.33 s + 4.248 -------------- s + 0.008496 Continuous-time transfer function.

Similarly, pick `Wnoise`

as a second-order high-pass filter with a magnitude of 1 at `10*desBW`

. This will force the open-loop gain `PC`

to roll-off with a slope of -2 for frequencies beyond `10*desBW`

.

NF = (10*desBW)/20; % numerator corner frequency DF = (10*desBW)*50; % denominator corner frequency Wnoise = tf([1/NF^2 2*0.707/NF 1],[1/DF^2 2*0.707/DF 1]); Wnoise = Wnoise/abs(freqresp(Wnoise,10*desBW))

Wnoise = 0.1975 s^2 + 0.6284 s + 1 ------------------------------ 7.901e-05 s^2 + 0.2514 s + 400 Continuous-time transfer function.

Verify that the bounds `1/Wperf`

and `1/Wnoise`

on `S`

and `T`

do enforce the desired bandwidth and roll-off.

bodemag(1/Wperf,'b',1/Wnoise,'r',{1e-2,1e3}), grid title('Performance and roll-off specifications') legend('Bound on |S|','Bound on |T|','Location','NorthEast')

Next use `connect`

to build the open-loop interconnection (block diagram in Figure 1 without the controller block). Specify each block appearing in Figure 1, name the signals coming in and out of each block, and let `connect`

do the wiring:

P.u = 'u'; P.y = 'yp'; Wperf.u = 'd'; Wperf.y = 'Wperf'; Wnoise.u = 'n'; Wnoise.y = 'Wnoise'; S1 = sumblk('e = -ym'); S2 = sumblk('y = yp + Wperf'); S3 = sumblk('ym = y + Wnoise'); G = connect(P,Wperf,Wnoise,S1,S2,S3,{'d','n','u'},{'y','e'});

`G`

is a 3-input, 2-output uncertain system suitable for robust controller synthesis with `dksyn`

.

The design is carried out with the automated robust design command `dksyn`

. The target bandwidth is 4.5 rad/s.

ny = 1; nu = 1; [C,CL,muBound] = dksyn(G,ny,nu); muBound

muBound = 1.1482

When the robust performance indicator `muBound`

is near 1, the controller achieves the target closed-loop bandwidth and roll-off. As a rule of thumb, if `muBound`

is less than 0.85, then the performance can be improved upon, and if `muBound`

is greater than 1.2, then the desired closed-loop bandwidth is not achievable for the specified plant uncertainty.

Here `muBound`

is approximately 1 so the objectives are met. The resulting controller `C`

has 18 states:

size(C)

State-space model with 1 outputs, 1 inputs, and 18 states.

Use the `reduce`

command to simplify this controller and approximate it with a 6th-order controller.

Cr = reduce(C,6); opt = bodeoptions; opt.Grid = 'on'; opt.PhaseMatching = 'on'; bodeplot(C,'b',Cr,'r--',opt) legend('18-state controller C','6-state controller Cr','Location','SouthWest')

Plot the open-loop responses of the plant models `p1`

through `p6`

with the simplified controller `Cr`

.

```
bodeplot(Parray*Cr,'g',{1e-2,1e3},opt);
```

Plot the responses to a step disturbance at the plant output. These are consistent with the desired closed-loop bandwidth and robust to the plant variations, as expected from a Robust Performance mu-value of approximately 1.

```
step(feedback(1,Parray*Cr),'g',10/desBW);
```

The same design process can be repeated for different closed-loop bandwidth values `desBW`

. Doing so yields the following results:

Using

`desBW`

= 8 yields a good design with robust performance`muBound`

of 1.09. The step responses across the`Parray`

family are consistent with a closed-loop bandwidth of 8 rad/s.

Using

`desBW`

= 20 yields a poor design with robust performance`muBound`

of 1.35. This is expected because this target bandwidth is in the vicinity of very large plant uncertainty. Some of the step responses for the plants`p1,...,p6`

are actually unstable.

Using

`desBW`

= 0.3 yields a poor design with robust performance`muBound`

of 2.2. This is expected because`Wnoise`

imposes roll-off past 3 rad/s, which is too close to the natural frequency of the unstable pole (2 rad/s). In other words, proper control of the unstable dynamics requires a higher bandwidth than specified.

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