document: Modelling of complete robot dynamics based on a multi-dimensional , RBF-like neural architecture 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 a priori-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
doc_bio_tags: O O B I I O O O O O O B I O O O O O O O B I O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O B I O O B I O O O O B I I I I
extractive_keyphrases: complete robot dynamics manipulator dynamics static neural networks neural architecture general system analysis recursive implementation online identification dynamic model complete 6 joint industrial robot
abstractive_keyphrases: multi-dimensional rbf-like neural architecture fixed grid based neuron placement b-spline polynomial basis functions online learning