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Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

Author

Listed:
  • 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)

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.

Suggested Citation

  • Francisco Beltran-Carbajal & Hugo Yañez-Badillo & Ruben Tapia-Olvera & Julio C. Rosas-Caro & Carlos Sotelo & David Sotelo, 2023. "Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems," Mathematics, MDPI, vol. 11(10), pages 1-26, May.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:10:p:2272-:d:1145803
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    References listed on IDEAS

    as
    1. Isaac Chairez & Arthur Mukhamedov & Vladislav Prud & Olga Andrianova & Viktor Chertopolokhov, 2022. "Differential Neural Network-Based Nonparametric Identification of Eye Response to Enforced Head Motion," Mathematics, MDPI, vol. 10(6), pages 1-12, March.
    2. Seng-Chi Chen & Van-Sum Nguyen & Dinh-Kha Le & Nguyen Thi Hoai Nam, 2014. "Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-18, November.
    3. Yuri E. Gliklikh, 2006. "Necessary and sufficient conditions for global-in-time existence of solutions of ordinary, stochastic, and parabolic differential equations," Abstract and Applied Analysis, Hindawi, vol. 2006, pages 1-17, April.
    4. Hugo Yañez-Badillo & Francisco Beltran-Carbajal & Ruben Tapia-Olvera & Antonio Favela-Contreras & Carlos Sotelo & David Sotelo, 2021. "Adaptive Robust Motion Control of Quadrotor Systems Using Artificial Neural Networks and Particle Swarm Optimization," Mathematics, MDPI, vol. 9(19), pages 1-28, September.
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    Cited by:

    1. David Marcos-Andrade & Francisco Beltran-Carbajal & Ivan Rivas-Cambero & Hugo Yañez-Badillo & Antonio Favela-Contreras & Julio C. Rosas-Caro, 2024. "Sliding Mode Speed Control in Synchronous Motors for Agriculture Machinery: A Chattering Suppression Approach," Agriculture, MDPI, vol. 14(5), pages 1-25, May.
    2. Daniel Galvan-Perez & Francisco Beltran-Carbajal & Ivan Rivas-Cambero & Hugo Yañez-Badillo & Antonio Favela-Contreras & Ruben Tapia-Olvera, 2023. "Motion-Tracking Control of Mobile Manipulation Robotic Systems Using Artificial Neural Networks for Manufacturing Applications," Mathematics, MDPI, vol. 11(16), pages 1-49, August.

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