FPGA Hardware Neural Control of CartPole and F1TENTH Race Car

Marcin Paluch; Florian Bolli; Xiang Deng; Antonio Rios-Navarro; Chang Gao; Tobi Delbruck

IROS 20250 citations

FPGA Hardware Neural Control of CartPole and F1TENTH Race Car

Marcin Paluch, Florian Bolli, Xiang Deng, Antonio Rios-Navarro, Chang Gao, Tobi Delbruck

Abstract

Latency and computational cost often limit the use of Nonlinear Model Predictive Control (NMPC) in real-time robotics. To address this limitation, our work investigates FPGA-implemented Neural Controllers (NC) trained through supervised learning, mimicking NMPC. We show that inexpensive embedded FPGA hardware is sufficient to implement these neural controllers for high-frequency control of robotic systems. We demonstrate kilohertz control rates for a cartpole and offload control to the FPGA hardware on the F1TENTH race car. The FPGA NC outperforms NMPC on the cartpole, due to the faster control rate afforded by faster NC inference. The code and hardware implementation for this paper are available at https://github.com/SensorsINI/Neural-Control-Tools.

BibTeX

@inproceedings{iros2025_fpgahardwareneur,
  title = {FPGA Hardware Neural Control of CartPole and F1TENTH Race Car},
  author = {Marcin Paluch and Florian Bolli and Xiang Deng and Antonio Rios-Navarro and Chang Gao and Tobi Delbruck},
  booktitle = {IROS 2025},
  year = {2025}
}