SM-NMPC: Sliding Mode-Based Nonlinear Model Predictive Control for UAVs Under Degraded Motor on Microcontrollers

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 combines 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.