4 citations · 7 across the 4 of their papers we have counts for
6 papers · 1 filter
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…
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 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-…