Process models are popular for describing system dynamics in many industries and apply to various production environments. The advantages of these models are that they are simple, support transport delay estimation, and the model coefficients have an easy interpretation as poles and zeros.
A simple SISO process model has a gain, a time constant, and a gain.
Here, Kp is the proportional gain. Tp1 is the time constant of the real pole, and Td is the transport delay (dead time).
In System Identification Toolbox™, the
idproc model provides the process model
structure and can represent process models with up to three poles and a zero.
For more information, see What Is a Process Model?
|System Identification||Identify models of dynamic systems from measured data|
|Estimate Process Model||Estimate continuous-time process model for single-input, single-output (SISO) system in either time or frequency domain in the Live Editor|
A process model is a simple continuous-time transfer function that describes linear system dynamics in terms of static gain, time constants, and input-output delay.
Use regularly sampled time-domain and frequency-domain data, and continuous-time frequency-domain data.
Specify model parameters and estimation options to use for estimating a process model.
Identify continuous-time transfer functions from single-input/single-output (SISO) data using the app.
Estimate first-order process models with fully free parameters and with a combination of fixed and free parameters.
Specify whether to estimate the same transfer function for all input-output pairs, or a different transfer function for each pair.
Configure the model structure by specifying the number of real or complex poles, and whether to include a zero, delay, and integrator.
Specify a noise model.
Specify how the algorithm treats initial conditions for estimation of model parameters.