NMPCM: Nonlinear Model Predictive Control on Resource-Constrained Microcontrollers

2025-07-28

AI Summary

Deploying full nonlinear model predictive control (NMPC) on resource-constrained microcontrollers remains challenging due to high computational demands. Method: This paper proposes NMPCM, a lightweight algorithmic framework enabling end-to-end, approximation-free, real-time NMPC execution on embedded platforms. It integrates accurate nonlinear dynamics modeling, a custom embedded optimization solver, and computation-control co-scheduling—constituting a system-level optimization strategy. Contribution/Results: NMPCM achieves millisecond-scale per-step solve times while preserving high-precision trajectory tracking and strong robustness. It is validated both in simulation and on a quadrotor UAV platform (STM32H7 microcontroller with ROS/Gazebo), supporting closed-loop control frequencies exceeding 50 Hz. The implementation is fully open-sourced, establishing a reproducible embedded NMPC paradigm for edge-intelligent control.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Implementing NMPC on resource-constrained microcontrollers efficiently
• Optimizing computational efficiency while maintaining control accuracy
• Achieving high-frequency NMPC execution in real-time systems

Innovation

• Efficient NMPC for microcontrollers
• Optimizes computational efficiency and accuracy
• Real-time high-frequency NMPC execution