Model-Predictive Control on Resource-Constrained Microcontrollers
Paper 1. TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
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. Our approach is based on the alternating direction method of multipliers (ADMM) and leverages the structure of the MPC problem for efficiency. We demonstrate TinyMPC’s effectiveness by benchmarking against the state-of-the-art solver OSQP, achieving nearly an order of magnitude speed increase, as well as through hardware experiments on a 27 gram quadrotor, demonstrating high-speed trajectory tracking and dynamic obstacle avoidance.
Paper 2. Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC
Conic constraints appear in many important control applications like legged locomotion, robotic manipulation, and autonomous rocket landing. However, current solvers for conic optimization problems have relatively heavy computational demands in terms of both floating-point operations and memory footprint, making them impractical for use on small embedded devices. We extend TinyMPC, an open-source, high-speed solver targeting low-power embedded control applications, to handle second-order cone constraints. We also present code-generation software to enable deployment of TinyMPC on a variety of microcontrollers. We benchmark our generated code against state-of-the-art embedded QP and SOCP solvers, demonstrating a two-order-of-magnitude speed increase over ECOS while consuming less memory. Finally, we demonstrate TinyMPC’s efficacy on the Crazyflie, a lightweight, resource-constrained quadrotor with fast dynamics.
TinyMPC is publicly available at https://tinympc.org.
Related Papers
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2024 December |
Code Generation for Conic Model-Predictive Control on Microcontrollers with TinyMPC
Conference on Decision and Control (CDC). Milan, Italy. (In Review) |
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2024 May |
TinyMPC: Model-Predictive Control on Resource-Constrained Microcontrollers
International Conference on Robotics and Automation (ICRA). Yokohama, Japan. (Accepted) |
People
Khai Nguyen
Sam Schoedel
Anoushka Alavilli
Elakhya Nedumaran
