论文标题
使用深钢筋学习中的非线性能量收获中的吸引子选择
Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning
论文作者
论文摘要
最近的研究工作表明，有意使用非线性可以增强能源收集系统的能力。非线性收获者中出现的主要挑战之一是，非线性通常会导致多个吸引者，具有可能共存的理想和不良反应。本文提出了基于翻译到旋转磁性传输的非线性能量收割机，并展示了具有不同电源电源输出水平的吸引者。此外，还提出了一种使用深钢筋学习的控制方法，以实现以有限的致动物共存吸引子之间的吸引子切换。
Recent research efforts demonstrate that the intentional use of nonlinearity enhances the capabilities of energy harvesting systems. One of the primary challenges that arise in nonlinear harvesters is that nonlinearities can often result in multiple attractors with both desirable and undesirable responses that may co-exist. This paper presents a nonlinear energy harvester which is based on translation-to-rotational magnetic transmission and exhibits coexisting attractors with different levels of electric power output. In addition, a control method using deep reinforcement learning was proposed to realize attractor switching between coexisting attractors with constrained actuation.
