Monte carlo logarithmic number system for Model Predictive Control

Panagiotis D. Vouzis, Mark G. Arnold, Sylvain Collange, Mayuresh V. Kothare

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Simple algorithms can be analytically characterized, but such analysis is questionable or even impossible for more complicated algorithms, such as Model Predictive Control (MPC). Instead, Monte Carlo Arithmetic (MCA) enables statistical experimentation with an algorithm during runtime for detection and mitigation of numerical anomalies. Previous studies of MCA have been limited to software floating point. This paper studies how MCA can be used in an FPGA implementation of the Logarithmic Number System (LNS), forming the Monte Carlo Logarithmic Number System (MCLNS). Simulation studies present how MCLNS affects the accuracy vs. performance of an MPC implementation, and synthesis results give an estimate of the cost of utilizing MCLNS in a Xilinx Virtex-IV FPGA.

Original languageEnglish (US)
Title of host publicationProceedings - 2007 International Conference on Field Programmable Logic and Applications, FPL
Pages453-458
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event2007 International Conference on Field Programmable Logic and Applications, FPL - Amsterdam, Netherlands
Duration: Aug 27 2007Aug 29 2007

Other

Other2007 International Conference on Field Programmable Logic and Applications, FPL
CountryNetherlands
CityAmsterdam
Period8/27/078/29/07

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Monte carlo logarithmic number system for Model Predictive Control'. Together they form a unique fingerprint.

Cite this