Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

Francisco Beltran-Carbajal, Hugo Yañez-Badillo, Ruben Tapia-Olvera, Julio C. Rosas-Caro, Carlos Sotelo () and David Sotelo
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Francisco Beltran-Carbajal: Departamento de Energía, Unidad Azcapotzalco, Universidad Autónoma Metropolitana, Azcapotzalco, Mexico City 02200, Mexico
Hugo Yañez-Badillo: Departamento de Investigación, TecNM: Tecnológico de Estudios Superiores de Tianguistenco, Tianguistenco 52650, Mexico
Ruben Tapia-Olvera: Departamento de Energía Eléctrica, Universidad Nacional Autónoma de México, Coyoacán, Mexico City 04510, Mexico
Julio C. Rosas-Caro: Facultad de Ingenieria, Universidad Panamericana, Alvaro del Portillo 49, Zapopan 45010, Mexico
Carlos Sotelo: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico
David Sotelo: Tecnologico de Monterrey, School of Engineering and Sciences, Ave. Eugenio Garza Sada 2501, Monterrey 64849, Mexico

Mathematics, 2023, vol. 11, issue 10, 1-26

Abstract: A new adaptive-like neural control strategy for motion reference trajectory tracking for a nonlinear electromagnetic suspension dynamic system is introduced. Artificial neural networks, differential flatness and sliding modes are strategically integrated in the presented adaptive neural network control design approach. The robustness and efficiency of the magnetic suspension control system on desired smooth position reference profile tracking can be improved in this fashion. A single levitation control parameter is tuned on-line from a neural adaptive perspective by using information of the reference trajectory tracking error signal only. The sliding mode discontinuous control action is approximated by a neural network-based adaptive continuous control function. Control design is firstly developed from theoretical modelling of the nonlinear physical system. Next, dependency on theoretical modelling of the nonlinear dynamic system is substantially reduced by integrating B-spline neural networks and sliding modes in the electromagnetic levitation control technique. On-line accurate estimation of uncertainty, unmeasured external disturbances and uncertain nonlinearities are conveniently evaded. The effective performance of the robust trajectory tracking levitation control approach is depicted for multiple simulation operating scenarios. The capability of active disturbance suppression is furthermore evidenced. The presented B-spline neural network trajectory tracking control design approach based on sliding modes and differential flatness can be extended to other controllable complex uncertain nonlinear dynamic systems where internal and external disturbances represent a relevant issue. Computer simulations and analytical results demonstrate the effective performance of the new adaptive neural control method.

Keywords: electromagnetic levitation; differential flatness; reference trajectory tracking; artificial neural networks; sliding modes (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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