论文标题
使用加强Q学习计划的最佳跟踪交换不情愿电动机驱动器的当前控制
Optimal Tracking Current Control of Switched Reluctance Motor Drives Using Reinforcement Q-learning Scheduling
论文作者
论文摘要
在本文中，研究了一种新型的Q学习调度方法，用于当前不情愿电动机（SRM）驱动器的当前控制器。 Q学习算法是一类强化学习方法，可以找到线性控制问题的最佳远期解决方案。本文将介绍一种新的计划学习算法，该算法利用Q核表位于SRM模型的非线性表面上，而无需涉及有关模型参数的任何信息来通过安排Infinite Horizon Linorear Quadratic Quadratic Quadratic Quadratic Quadratic tracters（LQT）来跟踪参考电流轨迹，该轨迹由Q-learthmears Q-Learearning Algorearning Algorearning。此外，提出了线性插值算法来指导LQT之间的LQT在训练有素的Q核之间，以确保随着状态变量在模型的非线性表面上的演变，以确保平滑响应。最后，提供了模拟和实验结果，以验证提出的控制方案的有效性。
In this paper, a novel Q-learning scheduling method for the current controller of switched reluctance motor (SRM) drive is investigated. Q-learning algorithm is a class of reinforcement learning approaches that can find the best forward-in-time solution of a linear control problem. This paper will introduce a new scheduled-Q-learning algorithm that utilizes a table of Q-cores that lies on the nonlinear surface of a SRM model without involving any information about the model parameters to track the reference current trajectory by scheduling infinite horizon linear quadratic trackers (LQT) handled by Q-learning algorithms. Additionally, a linear interpolation algorithm is proposed to guide the transition of the LQT between trained Q-cores to ensure a smooth response as state variables evolve on the nonlinear surface of the model. Lastly, simulation and experimental results are provided to validate the effectiveness of the proposed control scheme.
