Open AccessFuzzy-neural network compensator for Robot manipulator controlled by PD-like fuzzy system
Author(s) -
Turki Y. Abdalla,
Basil H. Jasim
Publication year - 2012
Publication title -
iraqi journal for electrical and electronic engineering/al-maǧallaẗ al-ʻirāqiyyaẗ al-handasaẗ al-kahrabāʼiyyaẗ wa-al-ilikttrūniyyaẗ
Language(s) - English
Resource type - Journals
eISSN - 2078-6069
pISSN - 1814-5892
DOI - 10.37917/ijeee.8.1.4
Subject(s) - control theory (sociology) , artificial neural network , fuzzy logic , controller (irrigation) , computer science , torque , fuzzy control system , control engineering , robot , engineering , control (management) , artificial intelligence , physics , agronomy , biology , thermodynamics
In this paper, high tracking performance control structure for rigid robot manipulator is proposed. PD-like Sugano type fuzzy system is used as a main controller, while fuzzy-neural network (FNN) is used as a compensator for uncertainties by minimizing suitable function. The output of FNN is added to the reference trajectories to modify input error space, so that the system robust to any change in system parameters. The proposed structure is simulated and compared with computed torque controller. The simulation study has showed the validity of our structure, also showed its superiority to computed torque controller.