The field of system identification uses statistical methods to build mathematical models of dynamical systems from measured data. System identification also includes the optimal design of experiments for efficiently generating informative data for fitting such models as well as model reduction. A common approach is to start from measurements of the behavior of the system and the external influences (inputs to the system) and try to determine a mathematical relation between them without going into many details of what is actually happening inside the system; this approach is called system identification.
Topic
System Identification
This topic includes the following resources and journeys:
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What Is System Identification? | System Identification, Part 1
Get an introduction to system identification that covers what it is and where it fits in the bigger picture. See how the combination of data-driven methods and physical intuition can improve...
See MoreSystem Identification: Theory for the User
From the Back Cover
The field's leading text, now completely updated.
Modeling dynamical systems ― theory, methodology, and applications.
Lennart Ljung's System Identification: Theory...
See MoreLinear System Identification | System Identification, Part 2
Learn how to use system identification to fit and validate a linear model to data that has been corrupted by noise and external disturbances Noise and disturbances can make it difficult to...
See MoreSystem Identification Overview
System identification is a methodology for building mathematical models of dynamic systems using measurements of the input and output signals of the system. This overview from Mathworks...
See MoreSystem Identification Methods
System Identification is the process of determining the model or the equations of motion for your system. This is incredibly important because basing a control system design off of a bad...
See MoreITCLI: An Interactive Tool for Closed-Loop Identification
The Interactive Tool for Closed-Loop Identification (ITCLI) is an interactive software tool for understanding SISO closed-loop identification using prediction-error techniques. The tool...
See MoreITCRI: An Interactive Software Tool for Control-Relevant Identification
The Interactive Tool for Control Relevant Identification (ITCRI) comprehensively captures the control-relevant identification process, from input design to closed-loop control, depicting...
See MorePerspectives on Control-Relevant Identification Through the Use of Interacti...
This paper presents a control-relevant identification methodology through an intuitive interactive tool called "Interactive Tool for Control Relevant Identification (ITCRI)". ITCRI...
See MoreNonlinear Model Identification
Mathwork overview page describing nonlinear model identification. Use nonlinear model identification when a linear model does not completely capture your system dynamics. You can identify...
See MoreITSIE: An Interactive Software Tool for System Identification Education
ITSIE is an Interactive Tool for System Identification Education. The tool is developed using Sysquake, a Matlab-like language with fast execution and excellent facilities for interactive...
See MoreManuscript about ITISE: an Interactive Software Tool for System Identificati...
The paper describes the conceptual basis, main features and functionality of an interactive software tool developed in support of system identification education and discovery.
This...
See MoreIntroduction to System Identification
In this webinar, you will have a unique chance to learn about system identification from a world-renowned subject expert, Professor Lennart Ljung. Professor Ljung will explain the basic...
See MoreMATLAB Example: Online Recursive Least Squares Estimation
This example shows how to implement an online recursive least squares estimator. You estimate a nonlinear model of an internal combustion engine and use recursive least squares to detect...
See MoreSystem Identification: Koopman with Control
This lecture provides an overview of the use of modern Koopman spectral theory for nonlinear control. In particular, we develop control in a coordinate system defined by eigenfunctions of...
See Morei-pIDtune: An interactive tool for integrated system identification and PID ...
i-pIDtune is an interactive software tool that integrates system identification and PID controller design. The tool supports experimental design and execution under plant-friendly conditions...
See MoreWhat are Nonlinear ARX Models?
This Mathworks page provides an overview of Nonlinear ARX Models.Nonlinear ARX models extend the linear ARX models to the nonlinear case. The structure of these models enables you to model...
See MoreLinear Model Identification Basics
This is a curated list of Mathworks products, examples, and topics that cover identifying linear models, selecting suitable model structures, constructing and modifying model object...
See MoreNonlinear System Identification | System Identification, Part 3
Learn about nonlinear system identification by walking through one of the many possible model options: A nonlinear ARX model. Brian Douglas covers the importance of adding an offset term to...
See MoreSystem Identification: Dynamic Mode Decomposition with Control
This lecture provides an overview of dynamic mode decomposition with control (DMDc) for full-state system identification. DMDc is a least-squares regression technique based on the singular...
See MoreWhat are Polynomial Models?
This Mathworks page provides an overview of polynomial models.
See MoreWhat Is Online Estimation?
This Mathworks document describes online estimation. Online estimation algorithms estimate the parameters and states of a model when new data is available during the operation of the...
See MoreSystem Identification: DMD Control Example
This lecture gives a Matlab example of dynamic mode decomposition with control (DMDc) for full-state system identification.
See MoreData based modeling of nonlinear dynamic systems using System Identification...
Using an engine throttle valve modeling example, this demo shares some perspectives on creation of nonlinear models of dynamic systems from the measurements of its input and outputs. It...
See MoreSystem Identification: Sparse Nonlinear Models with Control
This lecture explores an extension of the sparse identification of nonlinear dynamics (SINDy) algorithm to include inputs and control. The resulting SINDY with control (SINDYc) can be used...
See MoreTwo Tank System: C MEX-File Modeling of Time-Continuous SISO System
This MATLAB example shows how to perform IDNLGREY modeling based on C MEX model files. It uses a simple system where nonlinear state space modeling really pays off.
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