Attractor Selection in Nonlinear Energy Harvesting Using Deep Reinforcement Learning

2020/10/03 by Xue-She Wang, Brian P. Mann, Wang, Xue-She +1
Engineering · #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #FOS: Electrical engineering #Innovative Energy Harvesting Technologies #Machine Learning (cs.LG) #Systems and Control (eess.SY) #Underwater Vehicles and Communication Systems #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2010.01255

openalex publication_date 2020/10/03 · openalex created_date 2020/10/08 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Adam: A Method for Stochastic Optimization
• Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors
• Continuous control with deep reinforcement learning

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