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Comparison of neural and control theoretic techniques for nonlinear dynamic systems

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dc.contributor.author Huang, He
dc.date.accessioned 2012-11-15T20:25:37Z
dc.date.available 2012-11-15T20:25:37Z
dc.date.issued 1994-05
dc.identifier.uri http://hdl.handle.net/1912/5559
dc.description Submitted 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 1994 en_US
dc.description.abstract This 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.mimetype application/pdf
dc.language.iso en_US en_US
dc.publisher Massachusetts Institute of Technology and Woods Hole Oceanographic Institution en_US
dc.relation.ispartofseries WHOI Theses en_US
dc.subject Remote submersibles en_US
dc.subject Submersibles en_US
dc.subject Nonlinear control theory en_US
dc.title Comparison of neural and control theoretic techniques for nonlinear dynamic systems en_US
dc.type Thesis en_US
dc.identifier.doi 10.1575/1912/5559


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