Max Aehle, TU Kaiserslautern

Title:
Introduction to Model Predictive Control

Abstract:

Model Predictive Control (MPC) is a methodology for controller design. Assuming that the controlled system P (plant) is adequately approximated by another system M (model), MPC iteratively determines its control action by the following strategy: Compute a control sequence that is optimal for M over a certain prediction horizon, and send only the first part of it as actual control input to P. Iterate this.

This method has been employed in a paper by Aleksić-Roeßner, King et al (2014) to reduce vortex shedding behind a cylinder. We will review the authors’ derivation of the reduced-order model M approximating P, and present an introduction to different control methodologies culminating in MPC. Finally, a plan for (another) computational validation of their affirmative result is laid out.