Data-Driven Finite-Set Predictive Current Control via Deep Q-Learning for Permanent Magnet Synchronous Motor Drives
- Resource Type
- Conference
- Authors
- Tang, Zichun; Ma, Chenwei; Rodriguez, Jose; Garcia, Cristian; Song, Wensheng
- Source
- 2023 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) Predictive Control of Electrical Drives and Power Electronics (PRECEDE), 2023 IEEE International Conference on. :1-6 Jun, 2023
- Subject
- Power, Energy and Industry Applications
Current control
Training
Q-learning
Simulation
Switches
Predictive models
Permanent magnet motors
Data-driven control
deep reinforcement learning
DQN
PMSM
- Language
This paper proposes a finite-set current control strategy based on deep Q-learning for permanent magnet synchronous machine (PMSM) drives. Here, the model-based current prediction of conventional model predictive control is abandoned. Instead, the proposed method selects an optimal switching action in each control period for PMSM drives by training a Deep Q-Network (DQN) to approximate the optimal Q function. Simulations are conducted to demonstrate the effectiveness of the proposed method, showing close performance compared to the conventional finite control set model predictive current control (FCS-MPCC) method.