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dc.contributor.authorHuang, He
dc.date.accessioned2012-11-15T20:25:37Z
dc.date.available2012-11-15T20:25:37Z
dc.date.issued1994-05
dc.identifier.urihttp://hdl.handle.net/1912/5559
dc.descriptionSubmitted in partial fulfillment of the requirements for the degree of Master of Science at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution May 1994en_US
dc.description.abstractThis thesis compares classical nonlinear control theoretic techniques with recently developed neural network control methods based on the simulation and experimental results on a simple electromechanical system. The system has a configuration-dependent inertia, which contributes a substantial nonlinearity. The controllers being studied include PID, sliding control, adaptive sliding control, and two different controllers based on neural networks: one uses feedback error learning approach while the other uses a Gaussian network control method. The Gaussian network controller is tested only in simulation due to lack of time. These controllers are evaluated based on the amount of a priori knowledge required, tracking performance, stability guarantees, and computational requirements. Suggestions for choosing appropriate control techniques to one's specific control applications are provided based on these partial comparison results.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherMassachusetts Institute of Technology and Woods Hole Oceanographic Institutionen_US
dc.relation.ispartofseriesWHOI Thesesen_US
dc.subjectRemote submersiblesen_US
dc.subjectSubmersiblesen_US
dc.subjectNonlinear control theoryen_US
dc.titleComparison of neural and control theoretic techniques for nonlinear dynamic systemsen_US
dc.typeThesisen_US
dc.identifier.doi10.1575/1912/5559


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