Approximate LASSO Model Predictive Control for Resource Constrained Systems

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.