Meta-Reinforcement-Learning-Based Current Control of Permanent Magnet Synchronous Motor Drives for a Wide Range of Power Classes
Darius Jakobeit, Maximilian Schenke, Oliver Wallscheid
Abstract
Data-driven reinforcement learning-based controller schemes have much potential to aid the design of model-free control algorithms that can be trained without the necessity of plant-specific parameter knowledge. Unfortunately, the corresponding training phase is a time- and possibly money-consuming process which needs to be repeated whenever application to a new plant system is requested. To reduce the total training time for a large set of heterogeneous plant systems, this article proposes a meta-reinforcement learning-based approach that is to be utilized for control of hundreds of different permanent magnet synchronous motor drives ranging from a few watts to hundreds of kilowatts. So-called context variables carry the meta information about the set of considered drive systems. Their estimation using a corresponding artificial neural network as context approximator is a core aspect of this article. The context information allows the reinforcement learning-based control algorithm to automatically adapt itself to individual motor drives without requiring individual plant training. Since the found context variables can also be interpreted as an implicit system identification result they allow to determine irregular plant behavior (e.g., faulty drives) as an added bonus of the proposed meta-reinforcement learning scheme. Empirical results during this proof of concept successfully validate the potential of the proposed approach to drastically reduce the total training time and encourage further research.