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

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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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Acknowledgements

The authors are grateful for the support of the National Natural Science Foundation of China (No. 60574018).

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  1. College of Marine Electrical Engineering, Dalian Maritime University, Dalian, 116026, China

    Senkui Lu & Xingcheng Wang

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  1. Senkui Lu
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Correspondence to Xingcheng Wang.

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Lu, S., Wang, X. Adaptive neural network output feedback control of incommensurate fractional-order PMSMs with input saturation via command filtering and state observer. Neural Comput & Applic 33, 5631–5644 (2021). https://doi.org/10.1007/s00521-020-05344-1

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  • DOI: https://doi.org/10.1007/s00521-020-05344-1

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