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http://dx.doi.org/10.5302/J.ICROS.2005.11.6.524

A Novel Neural Network Compensation Technique for PD-Like Fuzzy Controlled Robot Manipulators  

Song Deok-Hee (충남대학교 메카트로닉스공학과)
Jung Seul (충남대학교 메카트로닉스공학과)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.11, no.6, 2005 , pp. 524-529 More about this Journal
Abstract
In this paper, a novel neural network compensation technique for PD like fuzzy controlled robot manipulators is presented. A standard PD-like fuzzy controller is designed and used as a main controller for controlling robot manipulators. A neural network controller is added to the reference trajectories to modify input error space so that the system is robust to any change in system parameter variations. It forms a neural-fuzzy control structure and used to compensate for nonlinear effects. The ultimate goal is same as that of the neuro-fuzzy control structure, but this proposed technique modifies the input error not the fuzzy rules. The proposed scheme is tested to control the position of the 3 degrees-of-freedom rotary robot manipulator. Performances are compared with that of other neural network control structure known as the feedback error learning structure that compensates at the control input level.
Keywords
3 degrees-of-freedom rotary robot; fuzzy logic controller; neural network controller; RCT; FEL;
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