@article{Li2022, 
author = {Yunfei Li and Shengbo Eben Li and Xingheng Jia and Shulin Zeng and Yu Wang},
title = {FPGA accelerated model predictive control for autonomous driving},
year = {2022},
journal = {Journal of Intelligent and Connected Vehicles},
volume = {5},
number = {2},
pages = {63-71},
keywords = {FPGA, Model predictive control, Autonomous driving, ZYNQ},
url = {https://www.sciopen.com/article/10.1108/JICV-03-2021-0002},
doi = {10.1108/JICV-03-2021-0002},
abstract = {PurposeThe purpose of this paper is to reduce the difficulty of model predictive control (MPC) deployment on FPGA so that researchers can make better use of FPGA technology for academic research.Design/methodology/approachIn this paper, the MPC algorithm is written into FPGA by combining hardware with software. Experiments have verified this method.FindingsThis paper implements a ZYNQ-based design method, which could significantly reduce the difficulty of development. The comparison with the CPU solution results proves that FPGA has a significant acceleration effect on the solution of MPC through the method.Research limitations implicationsDue to the limitation of practical conditions, this paper cannot carry out a hardware-in-the-loop experiment for the time being, instead of an open-loop experiment.Originality valueThis paper proposes a new design method to deploy the MPC algorithm to the FPGA, reducing the development difficulty of the algorithm implementation on FPGA. It greatly facilitates researchers in the field of autonomous driving to carry out FPGA algorithm hardware acceleration research.}
}