Nonlinear Model Predictive Control (NMPC) is a powerful approach for controlling highly dynamic robotic systems, as it accounts for system dynamics and optimizes control inputs at each step. However, its high computational complexity makes implementation on resource-constrained microcontrollers impractical. While recent studies have demonstrated the feasibility of Model Predictive Control (MPC) with linearized dynamics on microcontrollers, applying full NMPC remains a significant challenge. This work presents an efficient solution for generating and deploying NMPC on microcontrollers (NMPCM) to control quadrotor UAVs. The proposed method optimizes computational efficiency while maintaining high control accuracy. Simulations in Gazebo/ROS and real-world experiments validate the effectiveness of the approach, demonstrating its capability to achieve high-frequency NMPC execution in real-time systems. The code is available at: https://github.com/aralab-unr/NMPCM.

翻译：非线性模型预测控制（NMPC）是一种控制高动态机器人系统的强大方法，因为它考虑了系统动力学并在每一步优化控制输入。然而，其高计算复杂性使得在资源受限的微控制器上实现变得不切实际。尽管近期研究已证明在微控制器上使用线性化动力学进行模型预测控制（MPC）的可行性，但应用完整的NMPC仍然是一个重大挑战。本研究提出了一种在微控制器上生成和部署NMPC（NMPCM）以控制四旋翼无人机的有效解决方案。所提方法在保持高控制精度的同时优化了计算效率。在Gazebo/ROS中的仿真和真实世界实验验证了该方法的有效性，证明了其在实时系统中实现高频NMPC执行的能力。代码发布于：https://github.com/aralab-unr/NMPCM。