(Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, 1994-05)
Huang, He
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.
