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JuliaHub Modeling Platform - JuliaSim Control Non-Linear

Robust MPC for Systems with Uncertain Parameters 

Understand how robust MPC takes parameter uncertainty into account ensuring that the controller achieves good performance and constraint satisfaction despite uncertainty in the model

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Julia collaboration has taken a revolutionary leap forward with JuliaHub Projects.

Wednesday, April 19th | 10:30 AM ET (US)

Robust MPC for Systems with Uncertain Parameters

Model Predictive Control (MPC) is a powerful control technique, but it sometimes requires accurate models to produce good results. Robust MPC takes parameter uncertainty into account, making sure that the controller achieves good performance and constraint satisfaction despite uncertainty in the model.

Join us for this webinar, led by Dr. Fredrik Bagge Carlson to learn how to:

  • Design an MPC controller for a nonlinear system

  • Model systems with uncertain parameters

  • Design a robust version of the MPC controller that takes into account the modeled uncertainty  

Tune in to watch a demo of controlling a simple linear system consisting of a single integrator. We will implement two versions of the controller, the first one will use the nominal plant model only and the second controller will use a model with explicit parameter uncertainty.

Can't join us for the live event? Webinar registrants receive a recording of the webinar after the event.

Register now!



Meet Your Speaker

JuliaHub Modeling Platform Team - Dr. Fredrick Bagge Carlson, Senior Software Engineer

Dr. Fredrik Bagge Carlson

Senior Software Engineer

Dr. Fredrik Bagge Carlson is the lead of the JuliaSim Control-systems team at JuliaHub. He holds a PhD in Automatic Control from Lund University, and has 10 years of experience in the fields of modeling, control, system identification and robotics.


JuliaSim Control 

  • Model-predictive control (MPC) for linear and nonlinear systems

  • Robust MPC for uncertain systems

  • GUI apps for autotuning and model reduction

  • PID autotuning to automate workflows and quickly tune PID controllers

Model Discovery

Combine models with tools like DiffEqFlux and NeuralPDE to discover missing physics and generate digital twins.

Combine with Pre-Built Models and Digital Twins

Grab complete models from the JuliaSim Model Store and compose the pieces to accelerate the design process.

Specialized Numerical Environments

Use the latest numerical tools, like discontinuity-aware differential equation solvers, high-performance steady state solvers, and domain-specific environments.

Blending classical physical modeling with modern Scientific Machine Learning techniques.


JuliaSim is a next generation cloud-based simulation platform, combining the latest techniques in Scientific Machine Learning with equation-based digital twin modeling and simulation. Our modern ML-based techniques accelerate simulation by up to 500x, changing the paradigm of what is possible with computational design. The premise of the software is to facilitate the design and accelerate challenging real-life models of considerable complexity.


JuliaSim allows the user to import models directly from the Model Store (more information below) into the Julia environment, making it easy to build large complex simulations. The user-friendly GUI facilitates the process and makes simulation more accessible to a wider audience.


JuliaSim produces surrogates of blackbox (and regular) dynamical systems using Continuous Time Echo State Networks (CTESNs). This novel technique allows, amongst other features, for implicit training in parameter space to stabilize the ill-conditioning present in stiff systems.

Learn more about the JuliaSim Ecosystem

Julia Computing delivers JuliaSim as an answer to accelerating simulations through digital-twin (or surrogate) modeling. By blending classical, physical modeling with advanced scientific machine learning (SciML) techniques, JuliaSim provides a next-generation platform for building, accelerating, and analyzing models.