让无人机在微型控制器上实现实时非线性模型预测控制
NMPCM: Nonlinear Model Predictive Control on Resource-Constrained Microcontrollers
- 用高效算法在微控制器上实现非线性模型预测控制
- 在真实无人机上达成高频实时控制，频率达100Hz
- 适合嵌入式机器人控制开发人员参考
非线性模型预测控制（NMPC）能有效处理高度动态的机器人系统，通过考虑系统动力学并每步优化控制输入实现精准控制。然而，其高计算复杂度使其难以在资源受限的微控制器上部署。尽管已有研究展示在线性化动力学下在微控制器上实现模型预测控制的可行性，但完整应用非线性模型预测控制仍具挑战。本文提出一种高效生成与部署方法（NMPCM），可在微控制器上实现对四旋翼无人机的非线性模型预测控制。该方法在保持高控制精度的同时显著提升计算效率。通过Gazebo/ROS仿真和实际飞行实验验证，系统可实现实时高频率控制，达到100Hz的执行频率。代码已公开：https://github.com/aralab-unr/NMPCM。
原文摘要 · Abstract (English)
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.
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