Design of a Neural Super-Twisting Controller to Emulate a Flywheel Energy Storage System
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...
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| Další autoři: | , , , |
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| Médium: | Artículo |
| Jazyk: | en_US |
| Vydáno: |
2021
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| Témata: | |
| On-line přístup: | https://doi.org/10.3390/en14196416 https://www.mdpi.com/1996-1073/14/19/6416 |
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| Shrnutí: | 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. |
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