ABSTRACT:
Based on the advantage of using the reinforcement learning on process control, provided by the fact that it is not necessary to know the exact mathematical model and the structure of its uncertainties, this article approaches the possibility of improving the performances of the Permanent Magnet Synchronous Motor (PMSM) control system based on the Field Oriented Control (FOC) type control strategy, by using the correction signals provided by a trained reinforcement learning agent, which will be added to the control signals ud, uq, and iqref . The type of reinforcement learning used is the Deep Deterministic Policy Gradient (DDPG). The combination possibilities of these control structures are presented, and their superiority over the FOC type control strategy is validated by numerical simulations.
KEYWORDS:
1.
Permanent magnet motors
2.
Field oriented control
3.
Reinforcement learning
4.
Intelligent agent
5. Deep neural networks
SOFTWARE: MATLAB/SIMULINK
BLOCK DIAGRAM:
Fig. 1. Block diagram for FOC-type control of the PMSM based on reinforcement learning.
EXPECTED SIMULATION RESULTS:
Fig. 2. Time evolution for the numerical
simulation of the PMSM control system based on the FOC-type strategy.
Fig. 3. Time evolution for the numerical
simulation of the PMSM control system based on TD3 agent for the correction of udref
and uqref .
Fig. 4. Time evolution for the numerical
simulation of the PMSM control system based on TD3 agent for the correction of iqref.
Fig. 5. Time evolution for the numerical
simulation of the PMSM control system based on TD3 agent for the correction of udref,
uqref and iqref.
CONCLUSION:
This article presents the FOC-type control structure of a PMSM, which is improved in terms of performance by using a reinforcement learning technique. Thus, the comparative results are presented for the case where the reinforcement learning agent is properly trained and provides correction signals that will be added to the control signals ud, uq, and iqref. Numerical simulations are used to demonstrate the superiority of the control system that uses the reinforcement learning, and the following papers will study the possibilities for optimization in terms of the implementation of the reinforcement learning in the PMSM control.
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