Specifications

Working with Impulse-Response Models

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Working with Impulse-Response Models

You can use System Identification Toolbox software to estimate finite step-response

or finite impulse-response (FIR) plant models using measured data. Such models, also

known as nonparametric models (see [6] for example), are easy to determine from plant

data ([3] and [7]) and have intuitive appeal.

You use the impulseest function to estimate an FIR model from measured data.

The function returns an identified transfer function model, idtf. To design a model

predictive controller for the plant, you can convert the identified FIR plant model to a

numeric LTI model. However, this conversion usually yields a high-order plant, which

can degrade the controller design. This result is particularly an issue for MIMO systems.

For example, the estimator design can be affected by numerical precision issues with

high-order plants.

Model predictive controllers work best with low-order parametric models (see [5] for

example). Therefore, to design a model predictive controller using measured plant

data, you can use a parametric estimator, such as ssest. Then estimate a low-order

parametric plant model. Alternatively, you can initially identify a nonparametric model

using the data and then estimate a low-order parametric model for the nonparametric

model’s response. (See [10] for an example.)