[Submitted on 8 Sep 2026]
Title:AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones
View a PDF of the paper titled AccelMPC: High-Rate, Low-Power FPGA-Accelerated Model Predictive Control for Tiny Drones, by Andrea Grillo and 1 other authors
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Abstract:Unlocking the potential of tiny aerial robots requires order of magnitude improvements in the performance of embedded edge control. In particular, although recent cached model predictive control (MPC) solvers can handle the fast system dynamics and complex constraints required for agile drone flight, their computational demands remain prohibitive for resource-constrained robots, forcing prior implementations to operate at reduced control rates. AccelMPC overcomes this challenge through an end-to-end co-design approach that jointly optimizes the solver algorithm, numerical representation, hardware mapping, and physical integration. AccelMPC pairs a co-designed FPGA-accelerated alternating direction method of multipliers (ADMM)-based MPC solver with a custom 6g PCB, providing high-bandwidth communication for deployment on a 35g Crazyflie. Hardware experiments demonstrate 1 kHz onboard constrained MPC with dynamic obstacles, up to 15.6x faster solve times and 195.4x improvement in energy-delay product over state-of-the-art embedded microcontroller-based solvers, all while scaling to optimization problems with over 20,000 optimization variables and a comparable number of constraints. We release our PCB design files, firmware, and FPGA solver code open source.
Comments: 8 pages, 8 figures, 2 tables
Subjects:
Robotics (cs.RO); Systems and Control (eess.SY)
Cite as: arXiv:2609.09380 [cs.RO]
(or arXiv:2609.09380v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.09380
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Brian Plancher [view email] [v1] Tue, 8 Sep 2026 19:27:13 UTC (6,146 KB)
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