TinyMPC

model-predictive control on resource-constrained microcontrollers

TinyMPC is a high-speed convex MPC solver with a low memory footprint, targeting the microcontrollers common on small robots (Alavilli* et al., 2024). This work won the Best Paper Award in Automation at ICRA 2024.

TinyMPC running onboard a Crazyflie quadrotor.

Code is available on GitHub, and more details are at tinympc.org.

References

2024

ICRA

TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers

Anoushka Alavilli^*, Khai Nguyen^*, Sam Schoedel^*, Brian Plancher, and Zachary Manchester

In IEEE International Conference on Robotics and Automation (ICRA), 2024

*These authors contributed equally to this work.

Best Paper Award in Automation Abs arXiv Code Website

Winner of the Best Paper Award in Automation.

Model-predictive control (MPC) is a powerful tool for controlling highly dynamic robotic systems subject to complex constraints. However, MPC is computationally demanding, and is often impractical to implement on small, resource-constrained robotic platforms. We present TinyMPC, a high-speed MPC solver with a low memory footprint targeting the microcontrollers common on small robots.