过程模型

具有静态增益、时间常数和输入/输出延迟的低阶传递函数模型

过程模型广泛应用于描述许多行业的系统动态特性，并适用于各种生产环境。这些模型的优点是简单，支持传输延迟估计，并且模型系数可以很容易地解释为零极点。

一个简单的 SISO 过程模型具有增益、时间常数和传输延迟。

$s y s = \frac{K_{p}}{1 + T_{p 1} s} e^{- T_{d} s} .$

这里，K_p 是比例增益，T_p₁ 是实极点的时间常数，T_d 是传输延迟（死区时间）。

在 System Identification Toolbox™ 中，idproc 模型提供了过程模型结构，可以表示最多具有三个极点和一个零点的过程模型。

App

系统辨识	从测量数据辨识动态系统模型

使用实时编辑器在时域或频域中估计单输入单输出 (SISO) 系统的连续时间过程模型

`idproc`	Continuous-time process model with identifiable parameters
`procest`	Estimate process model using time-domain or frequency-domain data

`pem`	Prediction error minimization for refining linear and nonlinear models
`idpar`	Create parameter for initial states and input level estimation
`delayest`	根据数据估计时间延迟（死区时间）
`init`	Set or randomize initial parameter values

`getpvec`	Obtain model parameters and associated uncertainty data
`setpvec`	Modify values of model parameters
`getpar`	Obtain attributes such as values and bounds of linear model parameters
`setpar`	Set attributes such as values and bounds of linear model parameters

procestOptions Options set for procest

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 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.

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.

Build simple process models using System Identification Toolbox.