This article presents an approach to the controller design for electrical drives, which makes use of methods of deep reinforcement learning. Conventional control methods dominated the field for a long time, since they usually lead to control solutions with very robust and steady results. Yet, it often can be found that the overall control performance heavily correlates with the experience and education of the developing engineer. Moreover, conventional methods strongly depend on the available knowledge of the control system (e.g., plant model accuracy), which often causes the necessity for thorough identification methods. Real-time capability issues are also a present problem of sophisticated control approaches, such as model-predictive methods. Especially, in the domain of electrical drive train control, solving elaborate online optimization problems may be critical when very small plant time constants have to be considered. The methods of deep reinforcement learning will not only enable to acquire a suitable controller structure, but, moreover, the procedure will tune itself, which will allow for a more abstract level of investigation. This article presents a first proof of concept by means of controlling the phase currents of a permanent magnet synchronous motor in a field-oriented framework. The results found are promising and motivate further research in this field.

中文翻译：

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通过深度强化学习进行电气驱动控制器设计：概念验证


本文提出了一种利用深度强化学习方法的电力驱动控制器设计方法。传统的控制方法在很长一段时间内占据了该领域的主导地位，因为它们通常会产生具有非常稳健和稳定结果的控制解决方案。然而，我们经常发现整体控制性能与开发工程师的经验和教育密切相关。此外，传统方法强烈依赖于控制系统的可用知识（例如，对象模型精度），这通常导致需要彻底的识别方法。实时能力问题也是复杂控制方法（例如模型预测方法）当前的问题。特别是，在电气传动系统控制领域，当必须考虑非常小的工厂时间常数时，解决复杂的在线优化问题可能至关重要。深度强化学习的方法不仅能够获得合适的控制器结构，而且程序会自我调整，这将允许进行更抽象的研究。本文通过在磁场定向框架中控制永磁同步电机的相电流来首次验证概念。发现的结果很有希望并激励该领域的进一步研究。