Huang He

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  • Thesis
    Comparison of neural and control theoretic techniques for nonlinear dynamic systems
    (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.