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

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  • Luis A. Vázquez
  • Francisco Jurado
  • Alma Y. Alanís

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.

Suggested Citation

  • Luis A. Vázquez & Francisco Jurado & Alma Y. Alanís, 2015. "Decentralized Identification and Control in Real-Time of a Robot Manipulator via Recurrent Wavelet First-Order Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, May.
    • Handle: RePEc:hin:jnlmpe:451049
    DOI: 10.1155/2015/451049
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    Cited by:

    1. Daniel A. Magallón & Rider Jaimes-Reátegui & Juan H. García-López & Guillermo Huerta-Cuellar & Didier López-Mancilla & Alexander N. Pisarchik, 2022. "Control of Multistability in an Erbium-Doped Fiber Laser by an Artificial Neural Network: A Numerical Approach," Mathematics, MDPI, vol. 10(17), pages 1-20, September.
    2. Daniel A. Magallón & Carlos E. Castañeda & Francisco Jurado & Onofre A. Morfin, 2021. "Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System," Energies, MDPI, vol. 14(19), pages 1-23, October.

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