The “Laboratory for Mechatronic and Renewable Energy Systems (LMRES)” was founded in 2018 by Prof. Dr.-Ing. habil. Christoph M. Hackl. 

The research of the laboratory focuses on design, modelling, control and simulation of intelligent mechatronic and renewable energy systems in order to boost energy efficiency, fault-tolerance, extended functionality, robustness/reliability and safety of the considered components and systems.  

Its interdisciplinary expertise is based on the combination of engineering disciplines like machine design, electrical drives, power electronics and mechatronics and the mathematical discipline systems and control theory.

At the moment, we work on micro-grids (four-wire system) and electrical components of e.g. airborne wind energy systems, biogas power plants, small-scale and large-scale wind turbine systems, geothermal power plants, electric vehicles and industrial drives.

Our vision

We develop intelligent components and systems using an automated development framework covering specification, design, modeling, system identification, control, operation management and additional functionality.


Here you find our news.

Neue Publikation über „Virtuelle Synchronmaschinen“

Wir freuen uns sehr über die Annahme eines Journalartikels im IEEE Open Journal of Industrial Electronics Society, verfasst von Andre Thommessen und Prof. Dr.-Ing. Christoph M. Hackl. Unser Team des Labors für mechatronische und regenerative Energiesysteme (LMRES) an der Hochschule München (HM) hat erfolgreich die Virtuelle Synchronmaschinen (VSM) Regelung für Windkraftanlagen (WECS) mit doppelt-gespeisten Asynchronmaschinen …

New Publication: Multi-objective Hyperparameter Optimization of Artificial Neural Networks for Optimal Feedforward Torque Control of Synchronous Machines

We are thrilled to announce our latest research, authored by Niklas Monzen, Florian Stroebl, Prof. Dr. Herbert Palm, and Prof. Dr.-Ing. Christoph M. Hackl, published in IEEE Open Journal of Industrial Electronics Society. Our team successfully applied Multi-Objective Hyperparameter Optimization (MO-HPO) to identify the best Artificial Neural Network (ANN) architectures for the optimal feedforward torque …