Titlebook: Neural Networks in Robotics; George A. Bekey (Professor),Kenneth Y. Goldberg (A Book 1993 Springer Science+Business Media New York 1993 ex

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Some Preliminary Comparisons Between a Neural Adaptive Controller and a Model Reference Adaptive Conple and well defined first-order problem. We focus on the rate of convergence, and on the capacity to control non-linear and time-varying systems. Results from a first experiment show that the MRAC always converges faster and performs better for linear systems, but that its performances decline in c

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Stable Nonlinear System Identification Using Neural Network Models a stability theory approach to synthesizing and analyzing neural network based identification schemes. First static network architectures are combined with dynamical elements in the form of stable filters to construct a type of recurrent network configuration which is shown to be capable of approxi

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Neural Networks Learning Rules for Control: Uniform Dynamic Backpropagation, Heavy Adaptive Learningms. The Uniform Dynamic BackPropagation rule is based on a non regular optimization scheme (subgradient algorithm), and is devoted to the minimisation of a Min Max criterion, in a neural network synaptic matrix space. The Heavy Adaptive learning rule is a continuous hebbian learning rule, enabling a

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Parameter Learning and Compliance Control Using Neural Networks [.], the shortcomings of current control methods in dealing with such applications were elucidated, and a new robust control approach based on terminal sliding modes was introduced. In this paper, the problem of identifying uncertain environments for stable contact control is considered. For the pu

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