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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 11/2021

29.09.2020 | Original Article

Adaptive neural network output feedback control of incommensurate fractional-order PMSMs with input saturation via command filtering and state observer

verfasst von: Senkui Lu, Xingcheng Wang

Erschienen in: Neural Computing and Applications | Ausgabe 11/2021

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Abstract

In this paper, an adaptive neural network (NN) output feedback control is investigated for incommensurate fractional-order permanent magnet synchronous motors under the condition of input saturation. First, a NN state observer is presented to obtain the ‘virtual estimate’ of angle speed, where the unknown function is approximated by the NN. Then, in order to solve the input saturation problem, an auxiliary system is developed under fractional-order framework. Next, the command filtered technology with an error compensation mechanism is used to handle the ‘explosion of complexity’ in backstepping and remove the filtering errors. In addition, the frequency distributed model is utilized such that the Lyapunov theory is available in the backstepping design and the system stability is demonstrated. Finally, numerical simulations confirm the availability of the proposed design.
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Metadaten
Titel
Adaptive neural network output feedback control of incommensurate fractional-order PMSMs with input saturation via command filtering and state observer
verfasst von
Senkui Lu
Xingcheng Wang
Publikationsdatum
29.09.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 11/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05344-1

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