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...

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Linear 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...

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System 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...

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System 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...

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Introduction 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...

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Nonlinear 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...

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Online Fault Detection for a DC Motor

Program embedded processors to estimate parameters and detect changes in motor dynamics in real time using System Identification Toolbox™.

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Data-Driven Control: Linear System Identification

Overview lecture on linear system identification and model reduction. This lecture discusses how we obtain reduced-order models from data that optimally capture input--output dynamics.

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System Identification: Full-State Models with Control

This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and...

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System 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...

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System Identification: DMD Control Example

This lecture gives a Matlab example of dynamic mode decomposition with control (DMDc) for full-state system identification.

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System 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...

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Online and Recursive System Identification | System Identification, Part 4

Online system identification algorithms estimate the parameters and states of a model as new data is measured and available in real-time or near real-time. Brian Douglas covers what online...

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System Identification: Regression Models

This lecture provides an overview of modern data-driven regression methods for linear and nonlinear system identification, based on the dynamic mode decomposition (DMD), Koopman theory, and...

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Identifying Dominant Balance Physics from Data - Jared Callaham

This video illustrates a new algorithm to identify local dominant physical balance relations from multiscale spatiotemporal data.

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Frequency Response Analysis FRA and the Amplitude Ratio and Phase Angle

Process engineers model output response to inputs that oscillate via frequency response analysis (FRA). In this video, I'll go over amplitude ratios and phas...

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Visually Determining Transfer Functions

Process Control classes can get pretty hard to follow when you lose sight of what transfer functions really are. How do you get them in the first place?

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Peter Ponders PID - System Identification Basics

Transfer functions don't come with the machine you are trying to control. Transfer functions must be estimated using system identification techniques. With... See More

Finding Transfer Functions from Response Graphs

Given a system response to a unit step change, in this video I'll cover how we can derive the transfer function so we can predict how our system will respond...

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Peter Ponders PID - System Identification Advanced

System Identification of a more complex system with noise and truncation errors. This is the same open loop transfer function used in the root locus videohtt... See More