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 control (OFTC) of synchronous machines.
This innovative method allows for systematically identifying Pareto-optimal ANNs, providing an optimal trade-off between accuracy and computational effort. We trained these identified Pareto-optimal ANNs, implemented them into a real-time system, and successfully tested them on a nonlinear reluctance synchronous machine (RSM) in our lab.
Drawing on the latest findings from ANN approximation theory, Niklas, Florian, Prof. Palm, and Prof. Hackl present valuable design guidelines for a Pareto-optimal, ANN-based OFTC design, significantly limiting the search space of ANN architectures.
📄 To the article: 10.1109/OJIES.2024.3356721