Title:
Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes

Simple item page
dc.contributor.advisor Sadegh, Nader
dc.contributor.advisor Calise, Anthony J.
dc.contributor.author Shin, Yoonghyun en_US
dc.contributor.committeeMember J.V.R. Prasad
dc.contributor.committeeMember Lee, Kok-Meng
dc.contributor.committeeMember Book, Wayne J.
dc.contributor.department Mechanical Engineering en_US
dc.date.accessioned 2006-01-18T22:25:31Z
dc.date.available 2006-01-18T22:25:31Z
dc.date.issued 2005-11-28 en_US
dc.description.abstract Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named composite model reference adaptive control is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of pseudo-control hedging techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties. en_US
dc.description.degree Ph.D. en_US
dc.format.extent 3934495 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/7577
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Uncertainties en_US
dc.subject Nonlinear dynamic regimes
dc.subject Fighter aircraft
dc.subject High angle of attack
dc.subject Composite adaptive control
dc.subject Supermaneuverability
dc.subject Adaptive control
dc.subject Neural networks
dc.subject Nonlinear dynamic inversion
dc.subject.lcsh Uncertainty en_US
dc.subject.lcsh Nonlinear control theory en_US
dc.subject.lcsh Neural networks (Computer science) en_US
dc.subject.lcsh Fighter planes en_US
dc.subject.lcsh Dynamics en_US
dc.subject.lcsh Adaptive control systems en_US
dc.title Neural Network Based Adaptive Control for Nonlinear Dynamic Regimes en_US
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Sadegh, Nader
local.contributor.corporatename George W. Woodruff School of Mechanical Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 43b818a8-518a-4721-bffc-becb11ba04e0
relation.isOrgUnitOfPublication c01ff908-c25f-439b-bf10-a074ed886bb7
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
shin_yoonghyun_200512_phd.pdf
Size:
3.75 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Theses and Dissertations