Reinforcement Learning for Active Noise Control in a Hydraulic System Anderson, Eric Steward, Brian Steward, Brian
dc.contributor.department Mechanical Engineering
dc.contributor.department Agricultural and Biosystems Engineering
dc.contributor.department Human Computer Interaction 2021-03-03T22:47:52.000 2021-04-29T23:07:20Z 2021-04-29T23:07:20Z Fri Jan 01 00:00:00 UTC 2021 2022-02-01 2021-06-01
dc.description.abstract <p>Hydraulic pressure ripple in a pump, as a result of converting rotational power to fluid power, continues to be a problem faced when developing hydraulic systems due to the resulting noise generated. In this paper, we present simulation results from leveraging an actor-critic reinforcement learning method as the control method for active noise control in a hydraulic system. The results demonstrate greater than 96%, 81%, and 61% pressure ripple reduction for the first, second, and third harmonics, respectively, in a single operating point test, along with the advantage of feed forward like control for high bandwidth response during dynamic changes in the operating point. It also demonstrates the disadvantage of long convergence times while the controller is effectively learning the optimal control policy. Additionally, this work demonstrates the ancillary benefit of the elimination of the injection of white noise for the purpose of system identification in the current state of the art.</p>
dc.description.comments <p>This is a manuscript of an article published as Anderson, Eric R., and Brian L. Steward. "Reinforcement Learning for Active Noise Control in a Hydraulic System." <em>Journal of Dynamic Systems, Measurement, and Control</em> 143, no. 6 (2021): 061006. DOI: <a href="" target="_blank">10.1115/1.4049556</a>. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/
dc.identifier.articleid 2473
dc.identifier.contextkey 21928679
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath abe_eng_pubs/1188
dc.language.iso en
dc.source.bitstream archive/|||Fri Jan 14 19:00:12 UTC 2022
dc.source.bitstream archive/|||Fri Jan 14 19:00:14 UTC 2022
dc.source.uri 10.1115/1.4049556
dc.subject.disciplines Acoustics, Dynamics, and Controls
dc.subject.disciplines Agriculture
dc.subject.disciplines Bioresource and Agricultural Engineering
dc.subject.keywords Actuators
dc.subject.keywords Control equipment
dc.subject.keywords Hydraulic drive systems
dc.subject.keywords Noise (Sound)
dc.subject.keywords Noise control
dc.subject.keywords Pressure
dc.subject.keywords Pumps
dc.subject.keywords Reinforcement learning
dc.subject.keywords Signal processing
dc.subject.keywords Displacement
dc.subject.keywords Valves
dc.title Reinforcement Learning for Active Noise Control in a Hydraulic System
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication ef71fa01-eb3e-4e29-ade7-bcb38f2968b0
relation.isOrgUnitOfPublication 6d38ab0f-8cc2-4ad3-90b1-67a60c5a6f59
relation.isOrgUnitOfPublication 8eb24241-0d92-4baf-ae75-08f716d30801
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