Code Generation and Conic Constraints for Model-Predictive Control on Microcontrollers with Conic-TinyMPC

TL;DR

This paper introduces a fast, structure-exploiting solver for conic MPC on microcontrollers, enabling larger problems and real-time control in resource-limited robotic systems.

Contribution

It extends ADMM-based solvers to support second-order cones and provides C++ code generation, significantly improving speed and problem size handling for embedded MPC.

Findings

Achieves up to 142.7x speedup over existing solvers.

Enables fitting larger conic problems in microcontroller memory.

Successfully deployed on a quadrotor for trajectory tracking.

Abstract

Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware remains difficult. This challenge is magnified by expressive conic constraints, which offer greater modeling power but require significantly more computation than linear alternatives. To address this challenge, we extend recent work developing fast, structure-exploiting, cached solvers for embedded applications based on the Alternating Direction Method of Multipliers (ADMM) to provide support for second-order cones, as well as C++ code generation from Python, MATLAB, and Julia. Microcontroller benchmarks show that our solver provides up to a two-order-of-magnitude speedup, ranging from 10.6x to 142.7x, over state-of-the-art embedded solvers on QP and SOCP problems, and enables us to fit order-of-magnitude larger problems in memory. We validate our solver's deployed performance through simulation and hardware experiments, including trajectory tracking with conic constraints on a 27g Crazyflie quadrotor. Our open-source code is available at https://tinympc.org.