Reinforcement-Learning-Based Parameter Look-Up Table Generating Method for Optimal Torque Control of Induction Motors
Qi, Xing; Cao, Wenping; Aarniovuori, Lassi (2022-07-13)
Post-print / Final draft
Qi, Xing
Cao, Wenping
Aarniovuori, Lassi
13.07.2022
IEEE Transactions on Industrial Electronics
IEEE
School of Energy Systems
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© 2022 IEEE
© 2022 IEEE
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2022091559106
https://urn.fi/URN:NBN:fi-fe2022091559106
Tiivistelmä
In the optimal control of induction motors, it is a challenging task to maintain the optimal torque over the varying operation conditions. This paper proposes a parameter look-up table generating method, that can achieve an optimal torque over a wide range of currents and speeds, even though the commands of current are not set correctly. Based on the motor’s testing data, this method uses a reinforcement-learning algorithm to generate parameter look-up tables iteratively. Experimental results show that the proposed method can learn appropriate parameters from the running data to output an optimal torque. The comparative studies show that the proposed method can generate 5%-25% more torque than traditional model-based parameter estimation methods, over a wide range of currents and speeds. Furthermore, the proposed method has a faster convergence feature and a higher identification resolution than many conventional search-based methods.
Lähdeviite
Qi, X., Cao, W., Aarniovuori, L. (2022). Reinforcement-Learning-Based Parameter Look-Up Table Generating Method for Optimal Torque Control of Induction Motors. IEEE Transactions on Industrial Electronics. DOI: 10.1109/TIE.2022.3189103
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