mlsanityData to 7 Oct 2026

Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning

arXiv 2010.01255 ↗First posted 3 Oct 2020eess.SY cs.LG cs.SYcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Oct 2020.

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Citing papers per year: 2024: 15.

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