Using backpropagation with temporal windows to learn the dynamics of the CMU direct-drive arm II
Advances in neural information processing systems 1
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
Robot Dynamics and Control
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
Year 2000 Solutions for Dummies
Year 2000 Solutions for Dummies
Adaptive Neural Network Control of Robotic Manipulators
Adaptive Neural Network Control of Robotic Manipulators
Modelling and Control of Robot Manipulators
Modelling and Control of Robot Manipulators
Modeling of Robot Dynamics Based on a Multi-Dimensional RBF-Like Neural Network
ICIIS '99 Proceedings of the 1999 International Conference on Information Intelligence and Systems
Experimental Estimation of Model Error Bounds Based on Modified Stochastic Approximation
Journal of Intelligent and Robotic Systems
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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 apriori-knowledge about the identification problem. The model structure is substantially simplified by general system analysis independent of robot type. But also a lot of specific features of the utilised experimental robot are taken into account.A fixed, grid based neuron placement together with application of B-spline polynomial basis functions is utilised favourably for a very effective recursive implementation of the neural architecture. Thus, an online identification of a dynamic model is submitted for a complete 6 joint industrial robot.