SYSTEM IDENTIFICATION with MATLAB. Linear Models

In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. The toolbox provides several linear and nonlinear black-box model structures, which have traditionally been useful for representing dynamic systems.

This book develops the next tasks with linear models:

• “Black-Box Modeling”
• “Identifying Frequency-Response Models”
• “Identifying Impulse-Response Models”
• “Identifying Process Models”
• “Identifying Input-Output Polynomial Models”
• “Identifying State-Space Models”
• “Identifying Transfer Function Models”
• “Refining Linear Parametric Models”
• “Refine ARMAX Model with Initial Parameter Guesses at Command Line”
• “Refine Initial ARMAX Model at Command Line”
• “Extracting Numerical Model Data”
• “Transforming Between Discrete-Time and Continuous-Time Representations”
• “Continuous-Discrete Conversion Methods”
• “Effect of Input Intersample Behavior on Continuous-Time Models”
• “Transforming Between Linear Model Representations”
• “Subreferencing Models”
• “Concatenating Models”
• “Merging Models”
• “Building and Estimating Process Models Using System Identification Toolbox
• “Determining Model Order and Delay”
• “Model Structure Selection: Determining Model Order and Input Delay”
• “Frequency Domain Identification: Estimating Models Using Frequency Domain Data”
• “Building Structured and User-Defined Models Using System Identification Toolbox”