TY - GEN
T1 - Model Free Reinforcement Learning based Control of Permanent Magnet Synchronous Motor Drive
AU - Vikas,
AU - Yadav, Pankaj
AU - Singh, Bharat
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Permanent Magnet Synchronous Motor (PMSM) drive plays a vital role in multiple applications, however, controlling of PMSM is a very complex task due to the presence of multiple nonlinear motor parameters which are directly dependent on its speed and current control mechanism. Traditional control algorithms such as vector control are badly impacted by these parameter variations. This research work presents the improved control topology under hybrid deep-reinforcement learning, which is more robust to changes in the motor parameters and loading conditions. Presented algorithm explicitly does not require the explicit plant model for tuning its parameters. Two control topologies based on the deep deterministic policy gradient (DDPG) algorithm and deep Q network (DQN) are proposed for controlling the PMSM. Additionally, the objective function based on the weighted sum of error of the tracking d-axis and q-axis current is proposed for learning control topology parameters. Numerous experimental investigations on the proposed current control of a drive have been carried out to demonstrate its effectiveness. The result shows that the DDPG is more reliable and has higher d-axis and q-axis current tracking accuracy as compared to the deep Q-learning algorithm.
AB - Permanent Magnet Synchronous Motor (PMSM) drive plays a vital role in multiple applications, however, controlling of PMSM is a very complex task due to the presence of multiple nonlinear motor parameters which are directly dependent on its speed and current control mechanism. Traditional control algorithms such as vector control are badly impacted by these parameter variations. This research work presents the improved control topology under hybrid deep-reinforcement learning, which is more robust to changes in the motor parameters and loading conditions. Presented algorithm explicitly does not require the explicit plant model for tuning its parameters. Two control topologies based on the deep deterministic policy gradient (DDPG) algorithm and deep Q network (DQN) are proposed for controlling the PMSM. Additionally, the objective function based on the weighted sum of error of the tracking d-axis and q-axis current is proposed for learning control topology parameters. Numerous experimental investigations on the proposed current control of a drive have been carried out to demonstrate its effectiveness. The result shows that the DDPG is more reliable and has higher d-axis and q-axis current tracking accuracy as compared to the deep Q-learning algorithm.
KW - Deep deterministic policy gradient (DDPG)
KW - Deep Q network (DQN)
KW - Model-free
KW - Permanent magnet synchronous motor (PMSM)
UR - http://www.scopus.com/inward/record.url?scp=85174539022&partnerID=8YFLogxK
U2 - 10.1109/IC2E357697.2023.10262459
DO - 10.1109/IC2E357697.2023.10262459
M3 - Conference contribution
AN - SCOPUS:85174539022
T3 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
BT - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Computer, Electronics and Electrical Engineering and their Applications, IC2E3 2023
Y2 - 8 June 2023 through 9 June 2023
ER -