Adaptive Control of 3-DOF Delta Parallel Robot
Por:
Aguilar-Mejia, O., Escorcia-Hernandez, J. M., Tapia-Olvera, R., Minor-Popocatl, H., Valderrabano-Gonzalez, A.
Publicada:
1 ene 2019
Resumen:
In this paper an adaptive neural network controller is used to solve the
problem of tracking trajectories of a delta parallel robot (DPR) with
three degrees of freedom. This controller used an adaptive artificial
B-Spline neural network (BSNN) for online training. The BSNN improves
the performance of DPR on a closed loop and update the parameters of
control scheme online. This algorithm sets the control signal without
using a detailed mathematical model nor exact values of the parameters
of the DPR. The proposed adaptive controller was compared with a
traditional control based a PD+G contoller. Analytical and numerical
results prove the robust and efficient performance of the adaptive
neural network controller.
Filiaciones:
Aguilar-Mejia, O.:
(Corresponding Author), UPAEP Univ, Dept Invest & Posgrad, Puebla, Mexico
UPAEP Univ, Dept Invest & Posgrad, Puebla, Mexico
Escorcia-Hernandez, J. M.:
Univ Politecn Tulancingo, Tulancingo, Hidalgo, Mexico
Tapia-Olvera, R.:
Univ Nacl Autonoma Mexico, Cd De Mexico, Mexico
Minor-Popocatl, H.:
UPAEP Univ, Puebla, Mexico
Valderrabano-Gonzalez, A.:
Univ Panamer, Fac Ingn, Zapopan, Jalisco, Mexico
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