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Process Models

Low-order transfer function models with static gain, time constant, and input/output delay

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, they support transport delay estimation, and the model coefficients have easy interpretations as poles and zeros.

A simple SISO process model has a gain, a time constant, and a transport delay.


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 IdentificationIdentify models of dynamic systems from measured data

Live Editor Tasks

Estimate Process ModelEstimate continuous-time process model for single-input, single-output (SISO) system in either time or frequency domain in the Live Editor


expand all

idprocContinuous-time process model with identifiable parameters
procestEstimate process model using time or frequency data
pemPrediction error minimization for refining linear and nonlinear models
idparCreate parameter for initial states and input level estimation
delayestEstimate time delay (dead time) from data
initSet or randomize initial parameter values
getpvecObtain model parameters and associated uncertainty data
setpvecModify values of model parameters
getparObtain attributes such as values and bounds of linear model parameters
setparSet attributes such as values and bounds of linear model parameters
procestOptionsOptions set for procest


Process Model Basics

What Is a Process Model?

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.

Data Supported by Process Models

Use regularly sampled time-domain and frequency-domain data, and continuous-time frequency-domain data.

Estimate Process Models

Estimate Process Models Using the App

Specify model parameters and estimation options to use for estimating a process model.

Identify Low-Order Transfer Functions (Process Models) Using System Identification App

Identify continuous-time transfer functions from single-input/single-output (SISO) data using the app.

Estimate Process Models at the Command Line

Estimate first-order process models with fully free parameters and with a combination of fixed and free parameters.

Estimating Multiple-Input, Multi-Output Process Models

Specify whether to estimate the same transfer function for all input-output pairs, or a different transfer function for each pair.

Set Process Model Options

Process Model Structure Specification

Configure the model structure by specifying the number of real or complex poles, and whether to include a zero, delay, and integrator.

Disturbance Model Structure for Process Models

Specify a noise model.

Specifying Initial Conditions for Iterative Estimation Algorithms

Specify how the algorithm treats initial conditions for estimation of model parameters.

Featured Examples