Abstract Reactive control of a two-body point absorber using reinforcement learning
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Reactive control of a two-body point absorber using reinforcement learning

机译:利用强化学习对两点式减震器进行无功控制

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摘要

AbstractIn this article, reinforcement learning is used to obtain optimal reactive control of a two-body point absorber. In particular, the Q-learning algorithm is adopted for the maximization of the energy extraction in each sea state. The controller damping and stiffness coefficients are varied in steps, observing the associated reward, which corresponds to an increase in the absorbed power, or penalty, owing to large displacements. The generated power is averaged over a time horizon spanning several wave cycles due to the periodicity of ocean waves, discarding the transient effects at the start of each new episode. The model of a two-body point absorber is developed in order to validate the control strategy in both regular and irregular waves. In all analysed sea states, the controller learns the optimal damping and stiffness coefficients. Furthermore, the scheme is independent of internal models of the device response, which means that it can adapt to variations in the unit dynamics with time and does not suffer from modelling errors.HighlightsReinforcement learning is applied to the reactive control of a two-body WEC.The algorithm finds the optimal damping and stiffness coefficients.A time averaged approach is adopted.The strategy is model-free and adaptive.
机译: 摘要 在本文中,强化学习用于获得两体点吸收器的最佳反应控制。特别是,采用Q学习算法来最大化每种海况下的能量提取。控制器的阻尼系数和刚度系数逐步变化,观察到相关的回报,这对应于由于大位移而导致的吸收功率或损失的增加。由于海浪的周期性,在跨越几个波浪周期的时间范围内将产生的功率平均,从而丢弃了每个新事件开始时的瞬态效应。为了验证规则波和不规则波的控制策略,都开发了两体点吸收器的模型。在所有已分析的海况下,控制器都会学习最佳的阻尼系数和刚度系数。此外,该方案独立于设备响应的内部模型,这意味着它可以适应单位动态随时间的变化,并且不会遭受建模误差的影响。 突出显示 强化学习被应用于两体WEC的反应控制。 该算法找到了最佳的阻尼和刚度 一次 该策略是无模型且自适应的。

著录项

  • 来源
    《Ocean Engineering》 |2018年第15期|650-658|共9页
  • 作者单位

    Institute of Energy Systems, University of Edinburgh, Faraday Building, Colin Maclaurin Road,Wave Energy Scotland, 10 Inverness Campus, In verness,Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, 100 Montrose Street,College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus;

    Institute of Energy Systems, University of Edinburgh, Faraday Building, Colin Maclaurin Road;

    Wave Energy Scotland, 10 Inverness Campus, In verness;

    Department of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, 100 Montrose Street;

    College of Engineering, Mathematics and Physical Sciences, University of Exeter, Penryn Campus;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Reinforcement learning (RL); Q-learning; Reactive control; Point absorber; Wave energy converter (WEC);

    机译:强化学习(RL);Q学习;无功控制;点吸收器;波能转换器(WEC);

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