Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning
This work addresses a domain-specific challenge in energy harvesting systems by enabling control over nonlinear dynamics, though it is incremental as it builds on existing methods for attractor switching.
The paper tackled the problem of multiple attractors with varying power outputs in nonlinear energy harvesters by proposing a control method using deep reinforcement learning to switch between attractors with constrained actuation, achieving attractor selection to enhance energy harvesting efficiency.
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.