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

Daniel Galvan-Perez, Francisco Beltran-Carbajal (), Ivan Rivas-Cambero, Hugo Yañez-Badillo, Antonio Favela-Contreras and Ruben Tapia-Olvera
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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

Mathematics, 2023, vol. 11, issue 16, 1-49

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.

Keywords: robotics; mobile manipulation robotic systems; artificial neural networks; laser-based manufacturing; robust control; active disturbance control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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