Control of a Point Absorber Using Reinforcement Learning
Control of a Point Absorber Using Reinforcement Learning
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DOI：
10.1109/tste.2016.2568754
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发表时间：
2016-06
影响因子：
8.8
通讯作者：
E. Anderlini;D. Forehand;P. Stansell;Qing Xiao;M. Abusara
中科院分区：
文献类型：
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作者：
E. Anderlini;D. Forehand;P. Stansell;Qing Xiao;M. Abusara
这项工作提出了强化学习在点吸收器最佳电阻控制中的应用。选择无模型 Q 学习算法是为了最大化每种海况下的能量吸收。对控制器阻尼进行阶跃变化，观察过度运动的相关惩罚或奖励，即相关功率的增益。由于重力波的一般周期性，吸收功率在持续几个波周期的时间范围内取平均值。该算法的性能通过对点吸收器在规则和不规则波浪中的升沉运动进行数值模拟来评估。发现该算法在每种海况下都收敛于最佳控制器阻尼。此外，无模型方法确保算法能够适应设备流体动力学随时间的变化，并且不受建模误差的影响。
This work presents the application of reinforcement learning for the optimal resistive control of a point absorber. The model-free Q-learning algorithm is selected in order to maximise energy absorption in each sea state. Step changes are made to the controller damping, observing the associated penalty, for excessive motions, or reward, i.e. gain in associated power. Due to the general periodicity of gravity waves, the absorbed power is averaged over a time horizon lasting several wave periods. The performance of the algorithm is assessed through the numerical simulation of a point absorber subject to motions in heave in both regular and irregular waves. The algorithm is found to converge towards the optimal controller damping in each sea state. Additionally, the model-free approach ensures the algorithm can adapt to changes to the device hydrodynamics over time and is unbiased by modelling errors.