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Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System

Author

Listed:
  • Daniel A. Magallón

    (Centro Universitario de los Lagos, Universidad de Guadalajara, Lagos de Moreno 47460, Mexico)

  • Carlos E. Castañeda

    (Centro Universitario de los Lagos, Universidad de Guadalajara, Lagos de Moreno 47460, Mexico)

  • Francisco Jurado

    (Tecnológico Nacional de México/I.T. La Laguna, Torreón 27000, Mexico)

  • Onofre A. Morfin

    (Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico)

Abstract

In this work, a neural super-twisting algorithm is applied to the design of a controller for a flywheel energy storage system (FESS) emulator. Emulation of the FESS is achieved through driving a Permanent Magnet Synchronous Machine (PMSM) coupled to a shaft to shaft DC-motor. The emulation of the FESS is carried out by controlling the velocity of the PMSM in the energy storage stag and then by controlling the DC-motor velocity in the energy feedback stage, where the plant’s states of both electrical machines are identified via a neural network. For the neural identification, a Recurrent Wavelet First-Order Neural Network (RWFONN) is proposed. For the design of the velocity controller, a super-twisting algorithm is applied by using a sliding surface as the argument; the latter is designed based on the states of the RWFONN, in combination with the block control linearization technique to the control of the angular velocity from both machines in their respective operation stage. The RWFONN is trained online using the filtered error algorithm. Closed-loop stability analysis is included when assuming boundedness of the synaptic weights. The results obtained from Matlab/Simulink validate the performance of the proposal in the control of an FESS.

Suggested Citation

  • Daniel A. Magallón & Carlos E. Castañeda & Francisco Jurado & Onofre A. Morfin, 2021. "Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System," Energies, MDPI, vol. 14(19), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:19:p:6416-:d:651383
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    References listed on IDEAS

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    Cited by:

    1. Daniel A. Magallón & Rider Jaimes-Reátegui & Juan H. García-López & Guillermo Huerta-Cuellar & Didier López-Mancilla & Alexander N. Pisarchik, 2022. "Control of Multistability in an Erbium-Doped Fiber Laser by an Artificial Neural Network: A Numerical Approach," Mathematics, MDPI, vol. 10(17), pages 1-20, September.

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