System Identification with Matlab. Model Objects, Data Preparation and Process Models

by A. Taylor

System Identification Toolbox provides MATLAB functions, Simulink blocks, and an app for constructing mathematical models of dynamic systems from measured input-output data. It lets you create and use models of dynamic systems not easily modeled from first principles or specifications. You can use time-domain and frequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, and state-space models. The toolbox also provides algorithms for embedded online parameter estimation. The most important content that this book provides are the following: Choosing Your System Identification Approach What Are Model Objects? Model Objects Represent Linear Systems About Model Data Types of Model Objects Dynamic System Models Numeric Models Numeric Linear Time Invariant (LTI) Models Identified LTI Models Identified Nonlinear Models About Identified Linear Models What are IDLTI Models? Measured and Noise Component Parameterizations Linear Model Estimation Linear Model Structures About System Identification Toolbox Model Objects When to Construct a Model Structure Independently of Estimation Commands for Constructing Linear Model Structures Model Properties Available Linear Models Estimation Report Compare Estimated Models Using Estimation Report Analyze and Refine Estimation Results Using Estimation Report Imposing Constraints on Model Parameter Values Recommended Model Estimation Sequence Supported Models for Time- and Frequency-Domain Data Supported Models for Time-Domain Data Supported Models for Frequency-Domain Data Supported Continuous- and Discrete-Time Models Model Estimation Commands Modeling Multiple-Output Systems About Modeling Multiple-Output Systems Modeling Multiple Outputs Directly Modeling Multiple Outputs as a Combination of Single-Output Models Improving Multiple-Output Estimation Results by Weighing Outputs During Estimation Regularized Estimates of Model Parameters What Is Regularization? When to Use Regularization Choosing Regularization Constants Estimate Regularized ARX Model Using System Identification App Loss Function and Model Quality Metrics What is a Loss Function? Options to Configure the Loss Function Model Quality Metrics Regularized Identification of Dynamic Systems Data Import and Processing Supported Data Ways to Obtain Identification Data Ways to Prepare Data for System Identification Requirements on Data Sampling Representing Data in MATLAB Workspace Time-Domain Data Representation Time-Series Data Representation Frequency-Domain Data Representation Import Time-Domain Data into the App Import Frequency-Domain Data into the App Transform Data Identifying Process Models What Is a Process Model? Data Supported by Process Models Estimate Process Models Using the App and Command Line Building and Estimating Process Models Using System Identification Toolbox Process Model Structure Specification Estimating Multiple-Input, Multi-Output Process Models" Disturbance Model Structure for Process Models Specifying Initial Conditions for Iterative Estimation Algorithms

330 pages, Paperback

Published November 14, 2017