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 Identification||Identify models of dynamic systems from measured data|
Attività di Live Editor
|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|
Create Process Model
Model Initialization and Structure Parameters
Extract or Set Model Parameters
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.
- Identificazione delle funzioni di trasferimento di ordine basso (modelli di processo) utilizzando l’app System Identification
Identificare funzioni di trasferimento a tempo continuo da dati a ingresso singolo/uscita singola (SISO) utilizzando l’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.