Approximate LASSO Model Predictive Control for Resource Constrained Systems

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

3 Citations (Scopus)
67 Downloads (Pure)


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)
ISBN (Electronic)9781728138107
Publication statusPublished - 30 Nov 2020
Event9th Sensor Signal Processing for Defence 2020: from Sensor to Decision -
Duration: 15 Sept 202016 Sept 2020


Conference9th Sensor Signal Processing for Defence 2020
Abbreviated titleSSPD 2020


  • Approximate Computing
  • FPGA
  • MPC
  • Proximal Gradient Descent

ASJC Scopus subject areas

  • Artificial Intelligence
  • Signal Processing


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