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An Adaptive Neural Identifier with Applications to Financial and Welding Systems

  • Intelligent Control and Applications
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Abstract

This paper considers the online identification problem of uncertain systems. Based on parallel and series-parallel configurations with feedback and by using Lyapunov arguments, a unified identification algorithm is introduced to ensure the boundedness of all associated errors and convergence of the state estimation error to an arbitrary neighborhood of the origin. The main peculiarity of the proposed algorithm lies in allowing the adjustment of the identification transient by using parameters that are not related to the residual state error. Two examples are deemed to validate the theoretical results and show the relevance of the application of the proposed methodology for online weld geometry prediction.

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Change history

  • 25 May 2021

    The author name of Kevin Herman Muraro GULARTE has been given online erroneously as GUIARTE.

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Authors and Affiliations

  1. Departamento de Engenharia Elétrica, Universidade de Brasília, Brasília, DF, CEP 70910-900, Brazil

    Kevin Herman Muraro Gularte & José Alfredo Ruiz Vargas

  2. Departamento de Engenharia Mecânica, Universidade de Brasília, Brasília, DF, CEP 70910-900, Brazil

    Jairo José Muñoz Chávez & Sadek Crisóstomo Absi Alfaro

Authors
  1. Kevin Herman Muraro Gularte
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  2. Jairo José Muñoz Chávez
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  3. José Alfredo Ruiz Vargas
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  4. Sadek Crisóstomo Absi Alfaro
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Correspondence to Kevin Herman Muraro Gularte.

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Recommended by Associate Editor Yonghao Gui under the direction of Editor Guang-Hong Yang.

We would like to thank Guillermo Alvarez Bestard, Alysson Martins Almeida Silva, and Thompson Rogfel for their helpful feedback. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.

Kevin Herman Muraro Guiarte received his B.S. degree in Mechatronics Engineering from Universidade de Brasilia (UNB-2013) and master’s degree in Mechatronic Systems from Universidade de Brasilia (UNB-2018). His research interests include synchronization system, chaotic systems, adaptive control, neural networks, Lyapunov stability theory and system identification.

Jairo José Muñoz Chávez received his B.S. in physical engineering from Unicauca University in 2006, and his M.S in mechatronic systems from the University of Brasilia in 2014. He is a Ph.D. student and researcher in the area of mechatronics at the University of Brasilia. His areas of interest are image processing, neural networks, welding parameters, automation and control.

José Alfredo Ruiz Vargas is a Professor of Control Systems in the Department of Electrical Engineering, University of Brasilia. He received his Dr.Sc. degree in electronics and computer engineering from Aeronautics Institute of Technology in 2003. From 2016 to 2017, he was a Visiting Professor at University of Alberta, Edmonton, AB, Canada. His current research interests include online identification, state estimation and adaptive control.

Sadek Crisóstomo Absi Alfaro received degree in mechanical engineering from the Federal University of “Rio Grande do Sul” (UFRGS-1979), master’s degree in metallurgical engineering from the Federal University of “Minas Gerais” (UFMG-1983) and PhD in Welding Engineering (1989) and postdoctoral (1990), both, by the School of Industrial and Manufacturing Science-Cranfield Institute (UK). Since 1983 was Lecturer at Brasilia University. Hold the position of Full Professor at the University of Brasilia in mechanical/mechatronic engineering Department from 2003 to 2017, developing activities on undergraduate and post-graduate studies and research activities and coordinated research and development projects. Nowadays, he is a Senior Researcher at Brasilia University.

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Gularte, K.H.M., Chávez, J.J.M., Vargas, J.A.R. et al. An Adaptive Neural Identifier with Applications to Financial and Welding Systems. Int. J. Control Autom. Syst. 19, 1976–1987 (2021). https://doi.org/10.1007/s12555-020-0081-x

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