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Low-complexity architecture for cyber-physical systems model identification

Low-complexity architecture for cyber-physical systems model identification
Low-complexity architecture for cyber-physical systems model identification
We propose a low complexity architecture for cyber-physical system (CPS) model identification based on multiple-model adaptive estimation (MMAE) algorithms. The complexity reduction is achieved by reducing the number of multiplications in the filter banks of the MMAE algorithm present in the cyber component of the CPS. The architecture has been implemented using FPGA for 16, 32, 64 filter banks as part of position and velocity estimations of autonomous auto-mobile application. It has been found up to 78% reduction in multiplications is possible, which translates to the reduction of 39% LUTs, 13% FFs, 27% DSPs, and 43% power reduction when compared with the conventional architecture (without multiplications reduction) at 100MHz operating frequency. Furthermore, the proposed architecture is able to identify accurate model of auto-mobile application just within 510ns, in the presence of external disturbances and abrupt changes.
1549-7747
1-6
Vala, Charan Kumar
41279fa4-5cb5-469b-88ae-cc43a35c4325
French, Mark
22958f0e-d779-4999-adf6-2711e2d910f8
Acharyya, Amit
1f8a0620-1c00-4306-a64c-5185ede71f38
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d
Vala, Charan Kumar
41279fa4-5cb5-469b-88ae-cc43a35c4325
French, Mark
22958f0e-d779-4999-adf6-2711e2d910f8
Acharyya, Amit
1f8a0620-1c00-4306-a64c-5185ede71f38
Al-Hashimi, Bashir
0b29c671-a6d2-459c-af68-c4614dce3b5d

Vala, Charan Kumar, French, Mark, Acharyya, Amit and Al-Hashimi, Bashir (2018) Low-complexity architecture for cyber-physical systems model identification. IEEE Transactions on Circuits and Systems II: Express Briefs, 1-6. (doi:10.1109/TCSII.2018.2881481).

Record type: Article

Abstract

We propose a low complexity architecture for cyber-physical system (CPS) model identification based on multiple-model adaptive estimation (MMAE) algorithms. The complexity reduction is achieved by reducing the number of multiplications in the filter banks of the MMAE algorithm present in the cyber component of the CPS. The architecture has been implemented using FPGA for 16, 32, 64 filter banks as part of position and velocity estimations of autonomous auto-mobile application. It has been found up to 78% reduction in multiplications is possible, which translates to the reduction of 39% LUTs, 13% FFs, 27% DSPs, and 43% power reduction when compared with the conventional architecture (without multiplications reduction) at 100MHz operating frequency. Furthermore, the proposed architecture is able to identify accurate model of auto-mobile application just within 510ns, in the presence of external disturbances and abrupt changes.

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Accepted/In Press date: 10 November 2018
e-pub ahead of print date: 15 November 2018

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Local EPrints ID: 426354
URI: http://eprints.soton.ac.uk/id/eprint/426354
ISSN: 1549-7747
PURE UUID: a3d44393-6229-430d-ad39-d45abae5ad2e

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Date deposited: 23 Nov 2018 17:30
Last modified: 19 Jul 2019 16:51

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