Take random samples of a model with both tunable and uncertain blocks. Using uncertain blocks requires Robust Control Toolbox™. Random sampling of tunable blocks works the same way as shown in this example.

Create an uncertain model of $$G\left(s\right)=a/\left(\tau s+1\right)$$, where *a* is an uncertain parameter that varies in the interval [3,5], and $$\tau $$ = 0.5 +/- 30%. Also, create a tunable PI controller, and form a closed-loop system from the tunable controller and uncertain system.

T is a generalized state-space model with two uncertain blocks, `a`

and `tau`

, and one tunable block, `C`

. Sample `T`

at 20 random `(a,tau)`

pairs.

`Ts`

is a 20-by-1 array of `genss`

models. The tunable block `C`

, which is not sampled, is preserved in `Ts`

. The structure `samples`

has fields `samples.a`

and `samples.tau`

that contain the values at which those blocks are sampled.

Grouping `a`

and `tau`

into a cell array causes `rsampleBlock`

to sample them together, as `(a,tau)`

pairs. Sampling the blocks independently generates a higher-dimensionality arrays. For example, independently taking 10 random samples of `a`

and 5 samples of `tau`

generates a 10-by-5 model array.

TsInd =
10x5 array of generalized continuous-time state-space models.
Each model has 1 outputs, 1 inputs, 2 states, and the following blocks:
C: Parametric PID controller, 1 occurrences.
Type "ss(TsInd)" to see the current value, "get(TsInd)" to see all properties, and "TsInd.Blocks" to interact with the blocks.

In this array, `a`

varies along one dimension and `tau`

varies along the other.