Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network

Luis A. Vázquez, Francisco Jurado and Alma Y. Alanís

Mathematical Problems in Engineering, 2015, vol. 2015, 1-12

Abstract:

A decentralized recurrent wavelet first-order neural network (RWFONN) structure is presented. The use of a wavelet Morlet activation function allows proposing a neural structure in continuous time of a single layer and a single neuron in order to identify online in a series-parallel configuration, using the filtered error (FE) training algorithm, the dynamics behavior of each joint for a two-degree-of-freedom (DOF) vertical robot manipulator, whose parameters such as friction and inertia are unknown. Based on the RWFONN subsystem, a decentralized neural controller is designed via backstepping approach. The performance of the decentralized wavelet neural controller is validated via real-time results.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:451049

DOI: 10.1155/2015/451049

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