Multi-objective hyperparameter optimization of artificial neural networks for optimal feedforward torque control of synchronous machines
Multi-objective hyperparameter optimization (MO-HPO) is applied to find optimal artificial neural network (ANN) architectures used for optimal feedforward torque control (OFTC) of synchronous machines. The proposed framework allows to systematically identify Pareto optimal ANNs with respect to multiple (partly) contradictory objectives such as approximation accuracy and computational burden of the considered ANNs. The obtained Pareto optimal ANNs are trained and implemented on a realtime system and tested experimentally for a nonlinear reluctance synchronous machine (RSM) against non-Pareto optimal ANN designs and a state-of-the-art OFTC approach. Finally, based on the most recent results from ANN approximation theory, guidelines for Pareto optimal ANN-based OFTC design and implementation are provided.
Funding
German Research Foundation (DFG) Open Access Publication Costs (NE 1911/2-1)
History
Email Address of Submitting Author
niklas.monzen@hm.eduORCID of Submitting Author
0000-0003-4818-7815Submitting Author's Institution
University of Applied Sciences MunichSubmitting Author's Country
- Germany