Approximate LASSO Model Predictive Control for Resource Constrained Systems

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Abstract

Abstract—LASSO MPC is a popular method for solving optimal
control problems within a receding horizon. However, it is 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 state of the art convex optimisation
(CVX), but with Significant improvements to both logic cost and
memory bandwidth, up to 60% and 80% reduction respectively,
with up to 70% power saving.
Original languageEnglish
Publication statusAccepted/In press - 20 Jul 2020
EventInternational Conference in Sensor Signal Processing for Defence: from Sensor to Decision -
Duration: 15 Sep 202016 Sep 2020

Conference

ConferenceInternational Conference in Sensor Signal Processing for Defence
Abbreviated titleSSPD Conference 2020
Period15/09/2016/09/20

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