System identification with Julia: 2 Linear ARX models

We estimate a linear ARX model, also known as a discrete-time transfer function. System identification with Julia is an introductory video series about system identification, the art and science of estimating dynamical models using input-output data and the Julia programming language. Notebook file: juliahub.com/ui/Notebooks/fredrik-carlson2/system_… 00:00 Intro to linear models 00:53 Discrete and continuous time 04:00 The ARX model 06:45 Least-squares estimation 08:17 In practice 10:00 Constructing the regressor matrix 12:35 Computing the estimate 13:47 Using the built-in arx function 17:18 Consistency of the ARX least-squares estimate 20:23 Total least-squares estimation 22:31 Increasing the model order 23:12 Uncertainty quantification 25:33 Summary Throughout the series, we will use the tools JuliaSim and ControlSystemIdenitification, detailed below. The functions getARXregressor and arx used in the video belong to ControlSystemIdenitification.jl JuliaSim is an online platform providing enterprise solutions for modeling, simulation and control in Julia, building on ControlSystems.jl, see juliahub.com/products/juliasim/ with control-specific documentation available help.juliahub.com/juliasimcontrol/stable/ For classical system identification in Julia, see baggepinnen.github.io/ControlSystemIdentification.… Please leave a comment if there is a particular system-identification related topic you would like to learn about! Got questions or feedback? We'd love to hear from you! Fill out the contact form below, and our team will get back to you as soon as possible. share.hsforms.com/1ax_TNnP2S52Asa5uwXn_Zwcwyu9

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