Low-complexity architecture for cyber-physical systems model identification

Charan Kumar Vala, Mark French, Amit Acharyya, Bashir Al-Hashimi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1-6
Number of pages6
JournalIEEE Transactions on Circuits and Systems. Part 2: Express Briefs
Volume66
Issue number8
DOIs
Publication statusPublished - 15 Nov 2018

Fingerprint

Dive into the research topics of 'Low-complexity architecture for cyber-physical systems model identification'. Together they form a unique fingerprint.

Cite this