System Identification with MATLAB. Create Linear and Nonlinear Dynamic System Models

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 t [...]
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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 toolbox provides identification techniques such as maximum likelihood, prediction-error minimization (PEM), and subspace system identification. To represent nonlinear system dynamics, you can estimate Hammerstein-Weiner models and nonlinear ARX models with wavelet network, tree-partition, and sigmoid network nonlinearities. The toolbox performs grey-box system identification for estimating parameters of a user-defined model. You can use the identified model for system response prediction and plant modeling in Simulink. The toolbox also supports time-series data modeling and time-series forecasting. The most important content that this book provides are the following: -System Identification Overview -What Is System Identification? -About Dynamic Systems and Models -System Identification Requires Measured Data -Building Models from Data -Black-Box Modeling -Grey-Box Modeling -Evaluating Model Quality -When to Use the App vs. the Command Line -System Identification Workflow -Commands for Model Estimation -Linear Model Identification -Identify Linear Models Using System Identification App -Preparing Data for System Identification -Saving the Session -Estimating Linear Models Using Quick Start -Estimating Linear Models -Viewing Model Parameters -Exporting the Model to the MATLAB Workspace -Exporting the Model to the Linear System Analyzer -Identify Linear Models Using the Command Line -Preparing Data -Estimating Impulse Response Models -Estimating Delays in the Multiple-Input System -Estimating Model Orders Using an ARX Model Structure -Estimating Transfer Functions -Estimating Process Models -Estimating Black-Box Polynomial Models -Simulating and Predicting Model Output -Identify Low-Order Transfer Functions (Process Models) -Using System Identification App -What Is a Continuous-Time Process Model? -Preparing Data for System Identification -Estimating a Second-Order Transfer Function (Process Model) -with Complex Poles -Estimating a Process Model with a Noise Component -Viewing Model Parameters -Exporting the Model to the MATLAB Workspace -Simulating a System Identification Toolbox Model in Simulink Software -Estimating Models Using Frequency-Domain Data -Advantages of Using Frequency-Domain Data -Representing Frequency-Domain Data in the Toolbox -Preprocessing Frequency-Domain Data for Model -Estimation -Estimating Linear Parametric Models -Validating Estimated Model -Next Steps After Identifying a Model -Nonlinear Model Identification -Identify Nonlinear Black-Box Models Using System -Identification App -What Are Nonlinear Black-Box Models? -Preparing Data -Estimating Nonlinear ARX Models -Estimating Hammerstein-Wiener Models