Neural Adaptive Robust Motion-Tracking Control for Robotic Manipulator Systems
Por:
Galvan-Perez, Daniel, Yanez-Badillo, Hugo, Beltran-Carbajal, Francisco, Rivas-Cambero, Ivan, Favela-Contreras, Antonio, Tapia-Olvera, Ruben
Publicada:
1 sep 2022
Resumen:
This paper deals with the motion trajectory tracking control problem
based on output feedback and artificial neural networks for
anthropomorphic manipulator robots under disturbed operating scenarios.
This class of manipulator robots constitutes nonlinear dynamic systems
subjected to disturbance torques induced mainly by work payload.
Parametric uncertainty and possible dynamic modeling errors stand for
other kind of disturbances that can deteriorate the efficiency and
robustness of the tracking of controlled nonlinear robotic system
trajectories. In fact, the presence of unknown dynamic disturbances is
unavoidable in industrial robotic engineering systems. Therefore, for
high-precision applications, such as laser cutting, marking, or welding,
effective control schemes should be designed to guarantee adequate
motion profile tracking planned on this class of disturbed nonlinear
robotic system. In this context, a new adaptive robust motion trajectory
tracking control scheme based on output feedback and artificial neural
networks of anthropomorphic manipulator robots is presented. Three-layer
B-spline artificial neural networks and time-series modeling are
properly exploited in the design of novel adaptive robust motion
tracking controllers for robotic applications of laser manufacturing. In
this way, dependency on detailed nonlinear mathematical modeling of
robotic systems is considerably reduced, and real-time estimation of
uncertain dynamic disturbances is not required. Furthermore, several
cases studies to demonstrate the motion planning tracking control
robustness for a class of MIMO nonlinear robotic systems are described.
blue Insights for the extension of the introduced output-feedback
adaptive neural control design approach for other architecture of
nonlinear robotic systems are depicted.
Filiaciones:
Galvan-Perez, Daniel:
Univ Politecn Tulancingo, Dept Posgrad, Tulancingo De Bravo 43629, Hidalgo, Mexico
Yanez-Badillo, Hugo:
Tecnol Estudios Super Tianguistenco, Dept Invest, Santiago Tianguistenco 52650, Estado De Mexic, Mexico
Beltran-Carbajal, Francisco:
Univ Autonoma Metropolitana, Dept Energia, Unidad Azcapotzalco, Av San Pablo 180, Mexico City 02200, DF, Mexico
Rivas-Cambero, Ivan:
Univ Politecn Tulancingo, Dept Posgrad, Tulancingo De Bravo 43629, Hidalgo, Mexico
Favela-Contreras, Antonio:
Tecnol Monterrey, Sch Engn & Sci, Ave Eugenio Garza Sada 2501, Monterrey 64849, Nuevo Leon, Mexico
Tapia-Olvera, Ruben:
Univ Nacl Autonoma Mexico, Dept Energia Elect, Mexico City 04510, DF, Mexico
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