Approximate LASSO model predictive control for resource constrained systems

Yun Wu, Joao F.C. Mota, Andrew M. Wallace

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

3 Citations (Scopus)
28 Downloads (Pure)

Abstract

LASSO MPC is a popular method for solving optimal control problems within a receding horizon. It is, however, challenging to deploy LASSO MPC on resource constrained systems, such as embedded platforms, due to the intensive memory usage and computational cost as the horizon length is extended. By exploiting a reduced precision, approximation technique applied to Proximal Gradient Descent (PGD), we demonstrate an implementation on a resource constrained, reconfigurable device, such as a Field Programmable Gate Array (FPGA). Our experiments show equivalent performance to a high-precision optimisation solver, but with significant improvements to both logic cost and memory bandwidth, up to 60% and 80% reduction respectively, with up to 70% power savings.

Original languageEnglish
Title of host publication2020 Sensor Signal Processing for Defence Conference (SSPD): Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728138107
ISBN (Print)9781728138114
DOIs
Publication statusPublished - 30 Nov 2020
Externally publishedYes
Event9th Sensor Signal Processing for Defence Conference, SSPD 2020 - Edinburgh, United Kingdom
Duration: 15 Sept 202016 Sept 2020

Publication series

Name Sensor Signal Processing for Defence Conference (SSPD): Proceedings

Conference

Conference9th Sensor Signal Processing for Defence Conference, SSPD 2020
Country/TerritoryUnited Kingdom
CityEdinburgh
Period15/09/202016/09/2020

Bibliographical note

Funding Information:
Work supported by EPSRC Grant number EP/S000631/1 and the MOD University Defence Research Collaboration (UDRC) in Signal Processing.

Publisher Copyright:
© 2020 IEEE.

Keywords

  • Approximate Computing
  • FPGA
  • LASSO
  • MPC
  • Proximal Gradient Descent

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing

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