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Neural Block Control via Integrator Backstepping for a Robotic Arm in Real-Time

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Abstract

This paper presents an online neural identification and control scheme in continuous-time for trajectory tracking of a robotic arm evolving in the vertical plane. A recurrent high-order neural network (RHONN) structure in a block strict-feedback form is proposed to identify online in a series-parallel configuration, using the filtered error learning law, the dynamics of the plant. Based on the RHONN identifier structure, a stabilizing controller is derived via integrator backstepping procedure. The performance of the neural control scheme proposed is tested on a two degrees of freedom robotic arm, of our own design and unknown parameters, powered by industrial servomotors.

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Acknowledgements

This work was supported by CONACYT and TecNM Proyects.

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Authors and Affiliations

  1. TecNM/INSTITUTO TECNOLÓGICO de La Laguna, Blvd. Revolución y Av. INSTITUTO TECNOLÓGICO de La Laguna, Col. Centro, C.P. 27000, Torreón, Coahuila de Zaragoza, México

    Francisco Jurado, Luis A. Vázquez, Ramon Garcia-Hernandez & Miguel A. Llama

  2. Centro Universitario de los Lagos de la Universidad de Guadalajara, Av. Enrique Díaz de León No. 1144, Col. Paseo de la Montaña, C.P. 47460, Lagos de Moreno, Jalisco, México

    Carlos E. Castañeda

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  1. Francisco Jurado
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  2. Luis A. Vázquez
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  3. Carlos E. Castañeda
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  4. Ramon Garcia-Hernandez
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  5. Miguel A. Llama
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Correspondence to Carlos E. Castañeda.

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Jurado, F., Vázquez, L.A., Castañeda, C.E. et al. Neural Block Control via Integrator Backstepping for a Robotic Arm in Real-Time. Neural Process Lett 49, 1139–1155 (2019). https://doi.org/10.1007/s11063-018-9860-2

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