In many applications, it is useful to consider collections multiple model objects. For example, you may want to consider a model with a parameter that varies across a range of values, such as

sys1 = tf(1, [1 1 1]); sys2 = tf(1, [1 1 2]); sys3 = tf(1, [1 1 3]);

and so on. Model arrays are a convenient way to store and analyze
such a collection. Model arrays are collections of multiple linear
models, stored as elements in a single MATLAB^{®} array.

For all models collected in a single model array, the following attributes must be the same:

The number of inputs and outputs

The sample time

`Ts`

The time unit

`TimeUnit`

Uses of model arrays include:

Representing and analyzing sensitivity to parameter variations

Validating a controller design against several plant models

Representing linear models arising from the linearization of a nonlinear system at several operating points

Storing models obtained from several system identification experiments applied to one plant

Using model arrays, you can apply almost all of the basic model
operations that work on single model objects to entire sets of models
at once. Functions operate on arrays model by model, allowing you
to manipulate an entire collection of models in a vectorized fashion.
You can also use analysis functions such as `bode`

, `nyquist`

,
and `step`

to model arrays to analyze multiple models
simultaneously. You can access the individual models in the collection
through MATLAB array indexing.

To visualize the concept of a model array, consider the set of five transfer function models shown below. In this example, each model has two inputs and two outputs. They differ by parameter variations in the individual model components.

Just as you might collect a set of two-by-two matrices in a
multidimensional array, you can collect this set of five transfer
function models as a list in a model array under one variable name,
say, `sys`

. Each element of the model array is a
single model object.

The following illustration shows how indexing selects models
from a one-dimensional model array. The illustration shows a 1-by-5
array `sysa`

of 2-input, 2-output transfer functions.

The following illustration shows selection of models from the
two-dimensional model array `m2d`

.

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