129 citations · 359 across the 15 of their papers we have counts for
15 papers
RHINO-MAG: Recursive H-Field Inference based on Observed Magnetic Flux Density under Dynamic Excitation
Hendrik Vater, Oliver Schweins, Lukas Hölsch +3
Driven by the MagNet Challenge 2025 (MC2), increased research interest is directed towards modeling transient magnetic fields within ferrite materials. An accurate time-resolved an…
A Power Electronic Converter Control Framework Based on Graph Neural Networks -- An Early Proof-of-Concept
Darius Jakobeit, Oliver Wallscheid
Power electronic converter control is typically tuned per topology, limiting transfer across heterogeneous designs. This letter proposes a topology-agnostic meta-control framework…
HARDCORE: H-field and power loss estimation for arbitrary waveforms with residual, dilated convolutional neural networks in ferrite cores
Wilhelm Kirchgässner, Nikolas Förster, Till Piepenbrock +2
The MagNet Challenge 2023 calls upon competitors to develop data-driven models for the material-specific, waveform-agnostic estimation of steady-state power losses in toroidal ferr…
Distributed Control of Partial Differential Equations Using Convolutional Reinforcement Learning
Sebastian Peitz, Jan Stenner, Vikas Chidananda +3
We present a convolutional framework which significantly reduces the complexity and thus, the computational effort for distributed reinforcement learning control of dynamical syste…
Steady-State Error Compensation in Reference Tracking and Disturbance Rejection Problems for Reinforcement Learning-Based Control
Daniel Weber, Maximilian Schenke, Oliver Wallscheid
Reinforcement learning (RL) is a promising, upcoming topic in automatic control applications. Where classical control approaches require a priori system knowledge, data-driven cont…
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends
Shen Zhang, Oliver Wallscheid, Mario Porrmann
This review paper systematically summarizes the existing literature on utilizing machine learning (ML) techniques for the control and monitoring of electric machine drives. It is a…
Improved Exploring Starts by Kernel Density Estimation-Based State-Space Coverage Acceleration in Reinforcement Learning
Maximilian Schenke, Oliver Wallscheid
Reinforcement learning (RL) is currently a popular research topic in control engineering and has the potential to make its way to industrial and commercial applications. Correspond…
Thermal Neural Networks: Lumped-Parameter Thermal Modeling With State-Space Machine Learning
Wilhelm Kirchgässner, Oliver Wallscheid, Joachim Böcker
With electric power systems becoming more compact and increasingly powerful, the relevance of thermal stress especially during overload operation is expected to increase ceaselessl…
Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control
Henrik Bode, Stefan Heid, Daniel Weber +2
Micro- and smart grids (MSG) play an important role both for integrating renewable energy sources in conventional electricity grids and for providing power supply in remote areas.…
Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part I)
Sören Hanke, Oliver Wallscheid, Joachim Böcker
Two of the most important aspects of electric vehicles are their efficiency or achievable range. In order to achieve high efficiency and thus a long range, it is essential to avoid…
Data Set Description: Identifying the Physics Behind an Electric Motor -- Data-Driven Learning of the Electrical Behavior (Part II)
Sören Hanke, Oliver Wallscheid, Joachim Böcker
A data set was recorded to evaluate different methods for extracting mathematical models for a three-phase permanent magnet synchronous motor (PMSM) and a two-level IGBT inverter f…
Data-Driven Permanent Magnet Temperature Estimation in Synchronous Motors with Supervised Machine Learning
Wilhelm Kirchgässner, Oliver Wallscheid, Joachim Böcker
Monitoring the magnet temperature in permanent magnet synchronous motors (PMSMs) for automotive applications is a challenging task for several decades now, as signal injection or s…
Data-Driven Recursive Least Squares Estimation for Model Predictive Current Control of Permanent Magnet Synchronous Motors
Anian Brosch, Sören Hanke, Oliver Wallscheid +1
The performance of model predictive controllers (MPC) strongly depends on the model quality. In the field of electric drive control, white-box (WB) modeling approaches derived from…
Towards a Reinforcement Learning Environment Toolbox for Intelligent Electric Motor Control
Arne Traue, Gerrit Book, Wilhelm Kirchgässner +1
Electric motors are used in many applications and their efficiency is strongly dependent on their control. Among others, PI approaches or model predictive control methods are well-…
Koopman Operator-Based Finite-Control-Set Model Predictive Control for Electrical Drives
Sören Hanke, Sebastian Peitz, Oliver Wallscheid +3
Predictive control of power electronic systems always requires a suitable model of the plant. Using typical physics-based white box models, a trade-off between model complexity (i.…