Neural Network Based Control of Four-Bar Mechanism with Variable Input Velocity

R. Peón-Escalante, Manuel Flota-Bañuelos, Roberto Quintal-Palomo (), Luis J. Ricalde, F. Peñuñuri, B. Cruz Jiménez and J. Avilés Viñas
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R. Peón-Escalante: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico
Manuel Flota-Bañuelos: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico
Roberto Quintal-Palomo: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico
Luis J. Ricalde: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico
F. Peñuñuri: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico
B. Cruz Jiménez: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico
J. Avilés Viñas: Faculty of Engineering, University of Yucatan, Mérida 97000, Mexico

Mathematics, 2023, vol. 11, issue 9, 1-17

Abstract: For control applications, the angular velocity of the drive crank of a four-bar mechanism is traditionally assumed to be constant. In this paper, we propose control of variable velocity of the drive crank to obtain the desired output motions for the coupler point. To estimate the reference trajectory for the crank velocity, a neural network is trained with data from the kinematic model. The control law is designed from feedback linearization of the tracking error dynamics and a Proportional–Integral–Derivative (PID) controller. The applicability of the proposed scheme is validated through simulations for three variable speed profiles, obtaining excellent results from the system.

Keywords: four-bar mechanism; variable input-velocity; trajectory tracking; PID neural network controller (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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