Title:
Rotorcraft trim by a neural model-predictive auto-pilot

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dc.contributor.advisor Bottasso, Carlo
dc.contributor.author Riviello, Luca en_US
dc.contributor.committeeMember Bauchau, Olivier A.
dc.contributor.committeeMember Hodges, Dewey H.
dc.contributor.department Aerospace Engineering en_US
dc.date.accessioned 2005-07-28T18:02:29Z
dc.date.available 2005-07-28T18:02:29Z
dc.date.issued 2005-04-14 en_US
dc.description.abstract In this work we investigate the use of state-of-the-art tools for the regulation of complex, non-linear systems to improve the methodologies currently applied to trim comprehensive virtual prototypes of rotors and rotorcrafts. Among the several methods that have been proposed in the literature, the auto-pilot approach has the potential to solve trim problems efficiently even for the large and complex vehicle models of modern comprehensive finite element-based analysis codes. In this approach, the trim condition is obtained by adjusting the controls so as to virtually ``fly' the system to the final steady (periodic) flight condition. Published proportional auto-pilots show to work well in many practical instances. However, they cannot guarantee good performance and stability in all flight conditions of interest. Limit-cycle oscillations in control time histories are often observed in practice because of the non-linear nature of the problem and the difficulties in enforcing the constant-in-time condition for the controls. To address all the above areas of concern, in this research we propose a new auto-pilot, based on non-linear model-predictive control (NMPC). The formulation uses a non-linear reference model of the system augmented with an adaptive neural element, which identifies and corrects the mismatch between reduced model and controlled system. The methodology is tested on the wind-tunnel trim of a rotor multibody model and compared to an existing implementation of a classic auto-pilot. The proposed controller shows good performance without the need of a potentially very expensive tuning phase, which is required in classical auto-pilots. Moreover, model-predictive control provides a framework for guaranteeing stability of the non-linear closed-loop system, so it seems to be a viable approach for trimming complete rotorcraft comprehensive models in free-flight. en_US
dc.description.degree M.S. en_US
dc.format.extent 1381147 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/6930
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Multibody en_US
dc.subject Flight dynamics
dc.subject Reciding horizon
dc.subject Optimal control
dc.subject Tracking
dc.subject Predictive control
dc.subject Neural networks
dc.subject Aeroelasticity
dc.subject.lcsh Helicopters Control systems Design and construction en_US
dc.subject.lcsh Predictive control en_US
dc.subject.lcsh Automatic pilot (Airplanes) en_US
dc.subject.lcsh Control theory en_US
dc.subject.lcsh Flight control en_US
dc.title Rotorcraft trim by a neural model-predictive auto-pilot en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.corporatename College of Engineering
local.contributor.corporatename Daniel Guggenheim School of Aerospace Engineering
local.relation.ispartofseries Master of Science in Aerospace Engineering
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
relation.isOrgUnitOfPublication a348b767-ea7e-4789-af1f-1f1d5925fb65
relation.isSeriesOfPublication 09844fbb-b7d9-45e2-95de-849e434a6abc
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