Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/46751
Title: Robust neural controller for robot manipulator
Authors: Lou, Jia Ming
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Robotics
Issue Date: 2009
Abstract: Neural networks are widely used in industry fields, like robotic and process controllers. Extensive research and progress have also been done in various kinds of neural network algorithm, for example, the BP network, Hopfield network, RBF network, and a class of robust neural network algorithms. This thesis focuses on studying and developing robust online training and pruning algorithms for neural network tracking control systems. In particular, a complete convergence analysis is presented for Robust Adaptive Dead Zone training algorithm and Robust Adaptive Gradient Decent training algorithm, respectively.
Description: 67 p.
URI: http://hdl.handle.net/10356/46751
Rights: Nanyang Technological University
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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