Robot Dynamics and Control
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
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
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
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A modification of the estimation algorithm stochastic approximation is presented. With assumptions to the statistical distribution of the training data it becomes possible, to estimate not only the mean value but also well directed deviating values of the data distribution. Thus, detailed error models can be identified by means of parameter-linear formulation of the new algorithm. By definition of suitable probabilities, these parametric error models are estimating soft error bounds. That way, an experimental identification method is provided that is able to support a robust controller design. The method was applied at an industrial robot, which is controlled by feedback linearisation. Based on a dynamic model realised by a neural network, the presented approach is utilised for the robust design of the stabilising decentral controllers.