CMAC and B-spline neural networks applied to switched reluctance motor torque estimation and control

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Abstract

This paper describes the application of cerebellar model articulation controller (CMAC) and B-spline neural networks to switched reluctance motor (SRM) torque estimation and control. Non-linear adaptive systems such as neural networks are well suited to learning the highly non-linear electromagnetic characteristics of the SRM for the purposes of linearisation and simplification of their control and a number of researchers have investigated their use in this context. CMAC and B-spline neural networks are particularly suited to this application area due to their potential for low-cost, high-speed implementation including the capability for real-time, on-line adaptation. CMAC and B-spline neural networks have successfully been applied both to torque ripple minimisation and to torque estimation in simulation and, implemented using FPGA technology, experimentally. This paper describes those applications with particular emphasis on the suitability of the CMAC and B-spline neural networks and gives details of their FPGA implementation.

Original languageEnglish
Title of host publicationProceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003
Pages323-328
Number of pages6
Volume1
DOIs
Publication statusPublished - 2003
Event29th Annual Conference of the IEEE Industrial Electronics Society - Roanoke, VA, United States
Duration: 2 Nov 20036 Nov 2003

Conference

Conference29th Annual Conference of the IEEE Industrial Electronics Society
CountryUnited States
CityRoanoke, VA
Period2/11/036/11/03

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  • Cite this

    Reay, D. S. (2003). CMAC and B-spline neural networks applied to switched reluctance motor torque estimation and control. In Proceedings of the 29th Annual Conference of the IEEE Industrial Electronics Society, 2003 (Vol. 1, pp. 323-328) https://doi.org/10.1109/IECON.2003.1280001