SM-NMPC: Sliding Mode-Based Nonlinear Model Predictive Control for UAVs under Degraded Motor on Microcontrollers
Van Chung Nguyen, An Nguyen, Pratik Walunj, Chuong Le, Hung La
AI summary
Problem
Existing NMPC controllers require high-performance computing hardware, limiting their deployment on embedded UAV systems, while struggling to guarantee stability and handle motor failures in real-time.
Approach
The method integrates Aggregated Hierarchical Sliding Mode Control to provide a stabilizing virtual reference, which guides a Nonlinear Model Predictive Controller optimized via ACADO code generation and the qpOASES solver for microcontroller deployment.
Key results
- First real-time NMPC implementation on a resource-constrained microcontroller with Lyapunov stability guarantees
- Robust trajectory tracking maintained under single-propeller failure conditions
- Validated through Gazebo/ROS2 simulations and physical experiments on quadrotor and Cube-Drone platforms
- Open-source code and hardware configuration released for reproducibility
Why it matters
Provides a practical pathway for deploying robust, optimal control algorithms on lightweight, low-cost UAV hardware for safety-critical and embedded applications.
Abstract
This paper presents a novel Sliding Mode-Based Nonlinear Model Predictive Control (SM-NMPC) for controlling Unmanned Aerial Vehicles (UAVs) such as Quadrotors and a 10-propeller drone (Cube-Drone). The proposed method com- bines Aggregated Hierarchical Sliding Mode Control (AHSMC) strategies with Nonlinear Model Predictive Control (NMPC), designed to operate on resource-constrained microcontrollers. First, an AHSMC that provided a virtual input reference is introduced to ensure the UAV’s robustness, which is then leveraged by the NMPC to solve the optimization problem. A comprehensive comparison to existing approaches in terms of stability and computational efficiency demonstrates that the SM- NMPC framework excels, enabling quadrotor UAVs to accurately track reference trajectories even in the presence of a degraded motor. The proposed method also showcases the capability to implement robust optimal control on a microcontroller. Extensive experiments, both on real UAVs and their physical models in Gazebo/ROS2, are conducted to validate the effectiveness of the approach. A comparison to other state-of-the-art controllers further highlights the feasibility and superior performance of the proposed methodology. The open-source code has also been made available for further investigation: https://github.com/aralab-unr/ SM-NMPC.