Title:
Predictive Control of Multibody Systems for the Simulation of Maneuvering Rotorcraft

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dc.contributor.advisor Bottasso, Carlo
dc.contributor.author Sumer, Yalcin Faik en_US
dc.contributor.committeeMember Bauchau, Oliver A.
dc.contributor.committeeMember Hodges, Dewey H.
dc.contributor.department Aerospace Engineering en_US
dc.date.accessioned 2005-07-28T18:03:33Z
dc.date.available 2005-07-28T18:03:33Z
dc.date.issued 2005-04-18 en_US
dc.description.abstract Simulation of maneuvers with multibody models of rotorcraft vehicles is an important research area due to its complexity. During the maneuvering flight, some important design limitations are encountered such as maximum loads and maximum turning rates near the proximity of the flight envelope. This increases the demand on high fidelity models in order to define appropriate controls to steer the model close to the desired trajectory while staying inside the boundaries. A framework based on the hierarchical decomposition of the problem is used for this study. The system should be capable of generating the track by itself based on the given criteria and also capable of piloting the model of the vehicle along this track. The generated track must be compatible with the dynamic characteristics of the vehicle. Defining the constraints for the maneuver is of crucial importance when the vehicle is operating close to its performance boundaries. In order to make the problem computationally feasible, two models of the same vehicle are used where the reduced model captures the coarse level flight dynamics, while the fine scale comprehensive model represents the plant. The problem is defined by introducing planning layer and control layer strategies. The planning layer stands for solving the optimal control problem for a specific maneuver of a reduced vehicle model. The control layer takes the resulting optimal trajectory as an optimal reference path, then tracks it by using a non-linear model predictive formulation and accordingly steers the multibody model. Reduced models for the planning and tracking layers are adapted by using neural network approach online to optimize the predictive capabilities of planner and tracker. Optimal neural network architecture is obtained to augment the reduced model in the best way. The methodology of adaptive learning rate is experimented with different strategies. Some useful training modes and algorithms are proposed for these type of applications. It is observed that the neural network increased the predictive capabilities of the reduced model in a robust way. The proposed framework is demonstrated on a maneuvering problem by studying an obstacle avoidance example with violent pull-up and pull-down. en_US
dc.description.degree M.S. en_US
dc.format.extent 1648850 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/6940
dc.language.iso en_US
dc.publisher Georgia Institute of Technology en_US
dc.subject Vehicle dynamics en_US
dc.subject Flight mechanics
dc.subject Optimal control
dc.subject Trajectory optimization
dc.subject Maneuvers
dc.subject Multibody dynamics
dc.subject Trajectory tracking
dc.subject Neural networks
dc.subject Model predictive control
dc.subject.lcsh Trajectory optimization en_US
dc.subject.lcsh Helicopters Aerodynamics en_US
dc.subject.lcsh Helicopters Handling characteristics en_US
dc.subject.lcsh Predictive control en_US
dc.title Predictive Control of Multibody Systems for the Simulation of Maneuvering Rotorcraft 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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