Please use this identifier to cite or link to this item: http://rportal.lib.ntnu.edu.tw:80/handle/77345300/31934
Title: On-Line Genetic Algorithm-Based Fuzzy-Neural Sliding Mode Controller Using Improved Adaptive Bound Reduced-Form Genetic Algorithm
Authors: 國立臺灣師範大學電機工程學系
P.-Z. Lin
W.-Y. Wang
T.-T. Lee
C.-H. Wang
Issue Date: 1-Jun-2009
Publisher: Taylor & Francis
Abstract: In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.
URI: http://rportal.lib.ntnu.edu.tw/handle/77345300/31934
ISSN: 0020-7721
Other Identifiers: ntnulib_tp_E0604_01_012
Appears in Collections:教師著作

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