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Neural Computing and Applications

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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

Authors: Senkui Lu, Xingcheng Wang

Published in: Neural Computing and Applications | Issue 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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Title: Adaptive neural network output feedback control of incommensurate fractional-order PMSMs with input saturation via command filtering and state observer
Authors: Senkui Lu
Xingcheng Wang
Publication date: 29-09-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 11/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-020-05344-1

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