Tiny aerial robots hold great promise for applications such as environmental monitoring and search-and-rescue, yet face significant control challenges due to limited onboard computing power and nonlinear dynamics. Model Predictive Control (MPC) enables agile trajectory tracking and constraint handling but depends on an accurate dynamics model. While existing Learning-Based (LB) MPC methods, such as Gaussian Process (GP) MPC, enhance performance by learning residual dynamics, their high computational cost restricts onboard deployment on tiny robots. This paper introduces Tiny LB MPC, a co-designed MPC framework and optimization solver for resource-constrained micro multirotor platforms. The proposed approach achieves 100 Hz control on a Crazyflie 2.1 equipped with a Teensy 4.0 microcontroller, demonstrating a 43% average improvement in tracking performance over existing embedded MPC methods under model uncertainty, and achieving the first onboard implementation of LB MPC on a 53 g multirotor.

翻译：微型飞行机器人在环境监测与搜救等应用中展现出巨大潜力，但由于机载计算能力有限和非线性动力学特性，其控制面临重大挑战。模型预测控制（MPC）能够实现敏捷的轨迹跟踪与约束处理，但其性能依赖于精确的动力学模型。现有的基于学习的（LB）MPC方法（如高斯过程（GP）MPC）通过学习残差动力学提升了性能，然而其高昂的计算成本限制了在微型机器人上的机载部署。本文提出Tiny LB MPC，一种面向资源受限微型多旋翼平台协同设计的MPC框架与优化求解器。该方法在搭载Teensy 4.0微控制器的Crazyflie 2.1上实现了100 Hz的控制频率，在模型不确定性条件下，其跟踪性能相比现有嵌入式MPC方法平均提升43%，并首次在53克多旋翼平台上实现了LB MPC的机载部署。