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Robust MPC for Uncertain Parameters

On-Demand Webinar

Model Predictive Control (MPC) is a powerful control technique in data modeling, but it requires very accurate models to produce good results. Tuning the parameters of control systems can be tedious, especially when you're managing several parameters, and manual tuning is an arduous task. Robust MPC takes parameter uncertainty into account, making sure that the controller achieves good performance and constraint satisfaction despite uncertainty in the model.

In this video, you will 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

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.


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.

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