System Identification for PID Control
Plant Identification
In many situations, a dynamic representation of the system you want to control is not
readily available. One solution to this problem is to obtain a dynamical model using
identification techniques. The system is excited by a measurable signal and the
corresponding response of the system is collected at some sample rate. The resulting
input-output data is then used to obtain a model of the system such as a transfer function
or a state-space model. This process is called system identification or estimation. The goal
of system identification is to choose a model that yields the best possible fit between the
measured system response to a particular input and the model’s response to the same
input.If you have a Simulink model of your control system, you can simulate input/output data
instead of measuring it. The process of estimation is the same. The system response to
some known excitation is simulated, and a dynamical model is estimated based upon the
resulting simulated input/output data.Whether you use measured or simulated data for estimation, once a suitable plant model
is identified, you impose control objectives on the plant based on your knowledge of the
desired behavior of the system that the plant model represents. You then design a
feedback controller to meet those objectives.If you have System Identification Toolbox software, you can use PID Tuner for both plant
identification and controller design in a single interface. You can import input/output data
and use it to identify one or more plant models. Or, you can obtain simulated input/output
data from a Simulink model and use that to identify one or more plant models. You can
then design and verify PID controllers using these plants. PID Tuner also allows you to
directly import plant models, such as one you have obtained from an independent
identification task.For an overview of system identification, see About System Identification (System
Identification Toolbox).Linear Approximation of Nonlinear Systems for PID Control
The dynamical behavior of many systems can be described adequately by a linear
relationship between the system’s input and output. Even when behavior becomes