An Inversion-Based Neuro-Controller for Robot Manipulators
2019
Neuro-controllers that are applicable to a wider range of dynamical systems are the focus of this paper. The proposed controllers are inversion-based wherein the inverse of the dynamical system is modelled using a feedforward neural network with a single hidden layer and trained with the online sequential learning algorithm. We demonstrate that the proposed controller is applicable to both single- and twolink robot manipulators, and is able to handle flexible joints and unmodelled dynamics.
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