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.
