Deep Q Direct Torque Control (DQ-DTC) - Code Examples

Read the Paper

The jupyter notebooks in this folder were created to present the training and usage of a DQ-DTC reinforcement learning drive control system. A deep Q direct torque controller can be trained via the DQ_DTC_training.ipynb notebook. The trained controller can then be tested with the DQ_DTC_validation_profile.ipynb notebook. A benchmark for the performance of conventional control methods can be received via the MP_DTC_validation_profile.ipynb notebook.

It is recommended to use Python 3.7.1

The needed python packages are listed in the requirements.txt and can e.g. be installed via

    pip install -r requirements.txt

The python code files CustomKerasRL2Callbacks_torqueCtrl.py and Plot_TimeDomain_torqueCtrl.py implement extensions to the code that were outsourced from the notebooks, they do not need to be executed manually.

Citation

Please use the following BibTeX entry to cite this code:

@ARTICLE{9416143,  author={Schenke, Maximilian and Wallscheid, Oliver},  
journal={IEEE Open Journal of the Industrial Electronics Society},   
title={A Deep Q-Learning Direct Torque Controller for Permanent Magnet Synchronous Motors},   
year={2021},  
volume={2},  
number={},  
pages={388-400},  
doi={10.1109/OJIES.2021.3075521}}