Investigation of Data-Driven Koopman Model Predictive Control for Hydrogen-Diesel Engine Applications

This project explores the application of the Koopman Operator to system dynamics for the application within model predictive control of hydrogen-enhanced diesel engines. Investigating different implementation options and leveraging data-driven machine learning approaches, the aim is to reduce computational effort while enabling system-theoretic analysis of the dynamic system representation. The topic aligns closely with the interests of Prof. Jakob Andert (RWTH) and Prof. David Gordon (University of Alberta), whose expertise in machine learning-based control of energy conversion systems complements this work. Previous collaborations between these institutions have yielded valuable shared data and lab advancements. This project will further integrate RWTH’s machine learning modeling expertise with the University of Alberta’s embedded hardware knowledge, strengthening ongoing research efforts at both universities.

Faculty Supervisor:

David Gordon

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Engineering

Sector:

Education

University:

University of Alberta

Program: