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dc.contributor.authorYang, Bong-Junen_US
dc.date.accessioned2005-03-03T22:11:07Z
dc.date.available2005-03-03T22:11:07Z
dc.date.issued2004-04-12en_US
dc.identifier.urihttp://hdl.handle.net/1853/5248
dc.description.abstractNeural network-based adaptive output feedback approaches that augment a linear control design are described in this thesis, and emphasis is placed on their real-time implementation with flexible systems. Two different control architectures that are robust to parametric uncertainties and unmodelled dynamics are presented. The unmodelled effects can consist of minimum phase internal dynamics of the system together with external disturbance process. Within this context, adaptive compensation for external disturbances is addressed. In the first approach, internal model-following control, adaptive elements are designed using feedback inversion. The effect of an actuator limit is treated using control hedging, and the effect of other actuation nonlinearities, such as dead zone and backlash, is mitigated by a disturbance observer-based control design. The effectiveness of the approach is illustrated through simulation and experimental testing with a three-disk torsional system, which is subjected to control voltage limit and stiction. While the internal model-following control is limited to minimum phase systems, the second approach, external model-following control, does not involve feedback linearization and can be applied to non-minimum phase systems. The unstable zero dynamics are assumed to have been modelled in the design of the existing linear controller. The laboratory tests for this method include a three-disk torsional pendulum, an inverted pendulum, and a flexible-base robot manipulator. The external model-following control architecture is further extended in three ways. The first extension is an approach for control of multivariable nonlinear systems. The second extension is a decentralized adaptive control approach for large-scale interconnected systems. The third extension is to make use of an adaptive observer to augment a linear observer-based controller. In this extension, augmenting terms for the adaptive observer can be used to achieve adaptation in both the observer and the controller. Simulations to illustrate these approaches include an inverted pendulum with its cart serially attached to two carts (one unmodelled), three spring-coupled inverted pendulums, and an inverted pendulum with its initial condition in a range in which a linear controller is destabilizing.en_US
dc.format.extent2493007 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectActuator nonlinearityen_US
dc.subjectOutput feedback
dc.subjectAdaptive control
dc.subjectNeural networks
dc.subjectFlexible systems
dc.subject.lcshLinear control systemsen_US
dc.subject.lcshNeural networks (Computer science)en_US
dc.subject.lcshActuatorsen_US
dc.subject.lcshAdaptive control systemsen_US
dc.subject.lcshFeedback (Electronics)en_US
dc.titleAdaptive Output Feedback Control of Flexible Systemsen_US
dc.typeDissertationen_US
dc.description.degreePh.D.en_US
dc.contributor.departmentAerospace Engineeringen_US
dc.description.advisorCommittee Chair: Anthony J. Calise; Committee Member: James I. Craig; Committee Member: Naira Hovakimyan; Committee Member: Wassim M. Haddad; Committee Member: Wayne J. Booken_US


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