Open AccessRobust Adaptive Tracking Control for Manipulators Based on a TSK Fuzzy Cerebellar Model Articulation Controller
Author(s) -
Jiansheng Guan,
Chih-Min Lin,
Guo-Li Ji,
Ling-Wu Qian,
Yi-Min Zheng
Publication year - 2017
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2017.2779940
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The robot manipulator system is a complicated system with multiple-input and multiple-output, high nonlinearity, strong coupling, and uncertainties, such as parameter disturbances, external interference, and unmodeled dynamics. A robust adaptive Takagi-Sugeuo-Kang fuzzy cerebellar model articulation controller (RATFC) is proposed and applied to a robot manipulator to achieve high-precision position and speed control. A Takagi-Sugeuo-Kang fuzzy cerebellar model articulation controller is adopted, and the parameters are regulated by the derived adaptable rules according to a Lyapunov function. The robust compensation controller mitigates approximation-based errors. Finally, simulation results show that the proposed RATFC can achieve improved tracking performance compared with other neural network controllers.
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