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Motion-Tracking Control of Mobile Manipulation Robotic Systems Using Artificial Neural Networks for Manufacturing Applications

Author

Listed:
  • Daniel Galvan-Perez

    (Departamento de Posgrado, Universidad Politécnica de Tulancingo, Tulancingo de Bravo 43629, Mexico)

  • Francisco Beltran-Carbajal

    (Departamento de Energía, Universidad Autónoma Metropolitana, Unidad Azcapotzalco, Mexico City 02200, Mexico)

  • Ivan Rivas-Cambero

    (Departamento de Posgrado, Universidad Politécnica de Tulancingo, Tulancingo de Bravo 43629, Mexico)

  • Hugo Yañez-Badillo

    (Departamento de Investigación, TecNM—Tecnológico de Estudios Superiores de Tianguistenco, Tianguistenco 52650, Mexico)

  • Antonio Favela-Contreras

    (Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey 64849, Mexico)

  • Ruben Tapia-Olvera

    (Departamento de Energía Eléctrica, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico)

Abstract

Robotic systems have experienced exponential growth in their utilization for manufacturing applications over recent decades. Control systems responsible for executing desired robot motion planning face increasingly stringent performance requirements. These demands encompass high precision, efficiency, stability, robustness, ease of use, and simplicity of the user interface. Furthermore, diverse modern manufacturing applications primarily employ robotic systems within disturbed operating scenarios. This paper presents a novel neural motion-tracking control scheme for mobile manipulation robotic systems. Dynamic position output error feedback and B–Spline artificial neural networks are integrated in the design process of the introduced adaptive robust control strategy to perform efficient and robust tracking of motion-planning trajectories in robotic systems. Integration of artificial neural networks demonstrates performance improvements in the control scheme while effectively addressing common issues encountered in manufacturing environments. Parametric uncertainty, unmodeled dynamics, and unknown disturbance torque terms represent some adverse influences to be compensated for by the robust control scheme. Several case studies prove the robustness of the adaptive neural control scheme in highly coupled nonlinear six-degree-of-freedom mobile manipulation robotic systems. Case studies provide valuable insights and validate the efficacy of the proposed adaptive multivariable control scheme in manufacturing applications.

Suggested Citation

  • Daniel Galvan-Perez & Francisco Beltran-Carbajal & Ivan Rivas-Cambero & Hugo Yañez-Badillo & Antonio Favela-Contreras & Ruben Tapia-Olvera, 2023. "Motion-Tracking Control of Mobile Manipulation Robotic Systems Using Artificial Neural Networks for Manufacturing Applications," Mathematics, MDPI, vol. 11(16), pages 1-49, August.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:16:p:3489-:d:1215942
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    References listed on IDEAS

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    1. Francisco Beltran-Carbajal & Hugo Yañez-Badillo & Ruben Tapia-Olvera & Julio C. Rosas-Caro & Carlos Sotelo & David Sotelo, 2023. "Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems," Mathematics, MDPI, vol. 11(10), pages 1-26, May.
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