Adaptive Neuro-Fuzzy Control with Fast Learning Algorithm of PUMA560 Robot Manipulator

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This paper presents an investigation on trajectory control of a PUMA560 robot with six links derived by DC motors, using neural-fuzzy system with fast learning algorithm, in order to make a fuzzy logic system more adaptive and more effective. Such a compensatory fuzzy logic system is provide to be a universal approximator. Because built by both control-oriented fuzzy neurons and decision-oriented fuzzy neurons, if cannot only adaptively adjust fuzzy membership functions but also dynamically optimize the adaptive fuzzy reasoning by using a compensatory learning algorithm. The structure of control system has two blocs. The first is used to identifier the inverse dynamic model of the manipulator PUMA 560 by compensatory neural fuzzy networks and the second bloc is used to find an appropriate input that drives the manipulator to follow the desired trajectory, given by the sum of the outputs neural-fuzzy network and feedback controller. Simulation results provide adequate justification for current efforts to characterise unknown system using the neural-fuzzy system approach.
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Fuzzy Logic; Online Learning; Neural Networks; Neural-Fuzzy System; Adaptive Control

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