Comparison of neural and control theoretic techniques for nonlinear dynamic systems
Citable URI
https://hdl.handle.net/1912/5559DOI
10.1575/1912/5559Abstract
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.
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
Suggested Citation
Thesis: Huang, He, "Comparison of neural and control theoretic techniques for nonlinear dynamic systems", 1994-05, DOI:10.1575/1912/5559, https://hdl.handle.net/1912/5559Related items
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