Five phase multi-layer drive with fault tolerant neuro-fuzzy features for safety critical applications

Alessio Di Tullio; Marco Tursini; Francesco Parasiliti; Kan Akatsu

doi:10.1109/ICEMS.2017.8056487

Five phase multi-layer drive with fault tolerant neuro-fuzzy features for safety critical applications

Alessio Di Tullio, Marco Tursini, Francesco Parasiliti, Kan Akatsu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This article presents a method for control of a multiphase motor drive based on the use of a cognitive network that is capable of modelling nonlinearities of the system in fault conditions. To obtain a faithful model of the drive, a neural network (trained with the results of simulated faults) is used in the control algorithm in order to predict the system performance and adapt the control. A new multiphase motor with variable characteristics, particularly suited to applications in which a very wide variability of the speed range is required, will be described and presented in its structure. The technique to remedy failure adopted for this drive will be presented and described in its characteristics. The effectiveness of the control technique lastly will be highlighted by simulations that put it in comparison with classical solutions that employ PID controllers.

Original language	English
Title of host publication	2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538632468
DOIs	https://doi.org/10.1109/ICEMS.2017.8056487
Publication status	Published - 2017 Oct 2
Event	20th International Conference on Electrical Machines and Systems, ICEMS 2017 - Sydney, Australia Duration: 2017 Aug 11 → 2017 Aug 14

Other

Other	20th International Conference on Electrical Machines and Systems, ICEMS 2017
Country/Territory	Australia
City	Sydney
Period	17/8/11 → 17/8/14

Keywords

Fault tolerant
Multi-layer motor
Multiphase motor
Neural network
Neuro-fuzzy control
Safety critical application

ASJC Scopus subject areas

Energy Engineering and Power Technology
Electrical and Electronic Engineering
Mechanical Engineering
Control and Optimization

Access to Document

10.1109/ICEMS.2017.8056487

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Five phase multi-layer drive with fault tolerant neuro-fuzzy features for safety critical applications. / Di Tullio, Alessio; Tursini, Marco; Parasiliti, Francesco et al.

2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8056487.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Di Tullio, A, Tursini, M, Parasiliti, F & Akatsu, K 2017, Five phase multi-layer drive with fault tolerant neuro-fuzzy features for safety critical applications. in 2017 20th International Conference on Electrical Machines and Systems, ICEMS 2017., 8056487, Institute of Electrical and Electronics Engineers Inc., 20th International Conference on Electrical Machines and Systems, ICEMS 2017, Sydney, Australia, 17/8/11. https://doi.org/10.1109/ICEMS.2017.8056487

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N2 - This article presents a method for control of a multiphase motor drive based on the use of a cognitive network that is capable of modelling nonlinearities of the system in fault conditions. To obtain a faithful model of the drive, a neural network (trained with the results of simulated faults) is used in the control algorithm in order to predict the system performance and adapt the control. A new multiphase motor with variable characteristics, particularly suited to applications in which a very wide variability of the speed range is required, will be described and presented in its structure. The technique to remedy failure adopted for this drive will be presented and described in its characteristics. The effectiveness of the control technique lastly will be highlighted by simulations that put it in comparison with classical solutions that employ PID controllers.

AB - This article presents a method for control of a multiphase motor drive based on the use of a cognitive network that is capable of modelling nonlinearities of the system in fault conditions. To obtain a faithful model of the drive, a neural network (trained with the results of simulated faults) is used in the control algorithm in order to predict the system performance and adapt the control. A new multiphase motor with variable characteristics, particularly suited to applications in which a very wide variability of the speed range is required, will be described and presented in its structure. The technique to remedy failure adopted for this drive will be presented and described in its characteristics. The effectiveness of the control technique lastly will be highlighted by simulations that put it in comparison with classical solutions that employ PID controllers.

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Five phase multi-layer drive with fault tolerant neuro-fuzzy features for safety critical applications

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Keywords

ASJC Scopus subject areas

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