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Adaptive neural control for space structure vibration suppression

dc.contributor.authorDavis, Larry D.en_US
dc.contributor.authorHyland, David C.en_US
dc.contributor.authorYen, Garyen_US
dc.contributor.authorDas, Aloken_US
dc.date.accessioned2006-12-19T19:06:52Z
dc.date.available2006-12-19T19:06:52Z
dc.date.issued1999-12-01en_US
dc.identifier.citationDavis, Larry; Hyland, David; Yen, Gary; Das, Alok (1999). "Adaptive neural control for space structure vibration suppression." Smart Materials and Structures. 8(6): 753-766. <http://hdl.handle.net/2027.42/49009>en_US
dc.identifier.issn0964-1726en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/49009
dc.description.abstractDespite recent advances in efficiency, current methodologies for space structure control design still engage significant human resources for engineering development and routine maintenance. The adaptive neural control (ANC) program is part of an effort to develop neural network based controllers capable of self-optimization, on-line adaptation and autonomous fault detection and control recovery. This development in addition supports the long-term space exploration objectives for which autonomous spacecraft involving self-reliant control systems are a necessity. The ANC program comprises two phases. The first, basic phase focused on the development of efficient and completely autonomous neural network feedforward control for the case of broadband disturbances. Algorithms were developed that work with no prior modeling information about the system to be controlled and adapt to changing conditions, while minimizing or eliminating the introduction of extraneous training signals. The algorithms were demonstrated experimentally on an optical structure testbed at Harris. The second phase of the program demonstrated a more complex neural controller on the advanced space structures technology research experiments (ASTREX) test facility at the Air Force Research Laboratory capable of the fault-tolerant adaptive control of multiple sensors and actuators. This system used six actuation channels of the existing ACESA struts on the ASTREX structure to simultaneously cancel three independent tonal disturbances in the 10-15 Hz band, measured at non-collocated sensors on the secondary tower of the structure. The system demonstrated impressive fault-recovery performance, maintaining good cancellation performance with successive actuators disabled. Cancellation of individual tones was between 25 and 55 dB, with over 27 dB attenuation realized root mean square. The algorithm required very low computational throughput, operating at a sample rate of 1/20 Hz. The results of the ANC program show that adaptive cancellation systems can reduce vibrations in precision structures without prior modeling information and can adapt successfully to certain failures in actuators or sensors, optimally reconfiguring themselves without human intervention. These capabilities should significantly reduce the expense of designing and maintaining vibration control systems for spacecraft.en_US
dc.format.extent3118 bytes
dc.format.extent2842462 bytes
dc.format.mimetypetext/plain
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherIOP Publishing Ltden_US
dc.titleAdaptive neural control for space structure vibration suppressionen_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelPhysicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumUniversity of Michigan, Department of Aerospace Engineering, Ann Arbor, MI 48109, USAen_US
dc.contributor.affiliationumdag">Planning Systems, Inc., 2500 Harbor Street, Melbourne, FL 32902, USA University of Michigan, Department of Aerospace Engineering, Ann Arbor, MI 48109, USA Oklahoma State University, School of Electrical Engineering, Stillwater, OK 74078, USA USAF Research Laboratory, Space Vehicle Technology Division, Kirtland AFB, NM 87117, USAen_US
dc.contributor.affiliationotherPlanning Systems, Inc., 2500 Harbor Street, Melbourne, FL 32902, USAen_US
dc.contributor.affiliationotherOklahoma State University, School of Electrical Engineering, Stillwater, OK 74078, USAen_US
dc.contributor.affiliationumcampusAnn Arboren_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/49009/2/sm9605.pdfen_US
dc.identifier.doihttp://dx.doi.org/10.1088/0964-1726/8/6/305en_US
dc.identifier.sourceSmart Materials and Structures.en_US
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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