On Real-Time Feasibility of High-Rate MPC using an Active-Set Method on Nano-Quadcopters

Abstract

Deploying Model Predictive Control on nano-scale aerial platforms is challenging due to the severe computational limitations of onboard microcontrollers. This paper presents an experimental study with computational focus of a dual active-set solver (DAQP) applied to the low-level control of a Crazyflie 2.1 nano-quadcopter. Unlike previous approaches that utilize first-order methods for low-level stabilization or restrict active-set solvers to high-level control, this work demonstrates the successful deployment of a dual active-set method executing at 500 Hz on an STM32F405 microcontroller. We provide a direct benchmark against a state-of-the-art ADMM-based solver (TinyMPC), revealing that the active-set method yields lower execution times for the flight envelopes considered. Furthermore, to manage the complexity of certifying the solver's real-time feasibility, we introduce a data-driven set selection method using Principal Component Analysis. Using this approach, state-of-the-art real-time certification methods can be applied to confirm or reject real-time feasibility already off-line prior to flight. More generally, the experimental results validate that active-set methods are a highly competitive alternative for high-frequency control on resource-constrained hardware and the possibility for their real-time certification adds reliability.