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A modelling of robot manipulator dynamics by means of a neural architecture is presented. Such model is applicable to generate a decoupling and linearising feedback in the control system of the robot. In a structured model approach, a RBF-like neural network is used to represent and adapt all model parameters with their dependences on the joint positions.(More)
A dynamic modeling of a robot manipulator by means of a neural architecture is presented. Such model is applicable to generate a decoupling and linearizing feedback in the control loop of the robot drives. A modified extended Self-Organizing Map is used to perform the needed mapping of the robot's movement state to the according joint torques. To get(More)
A neural network based identification approach of manipulator dynamics is presented. For a structured modelling, RBF-like static neural networks are used in order to represent and adapt all model parameters with their non-linear dependences on the joint positions. The neural architecture is hierarchically organised to reach optimal adjustment to structural(More)
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