Scholarlink

<p>This paper investigates the computational challenges associated with Model Predictive Control (MPC) for trajectory tracking of quadrotor Unmanned Aerial Vehicles (UAVs). Despite its superior control performance, MPC is difficult to implement in real-time applications on resource-limited embedded systems due to its high computational complexity. Prior research has introduced explicit MPC methods to accelerate MPC computation, but these methods are generally ineffective in embedded applications involving complex systems, such as UAVs, which require high-frequency, real-time control. To overcome these limitations, this study proposes a novel MPC framework for quadrotor UAV trajectory tracking, specifically tailored for embedded GPUs. The method integrates Lyapunov Guidance Vectors (LGV) and Deep Neural Networks (DNN) to significantly reduce computation time. Our approach reduces the computation time to about one thirty-fifth of that required by conventional MPC methods, while still maintaining accurate trajectory tracking performance. Additionally, we validate the effectiveness of the proposed method through Hardware-in-the-Loop (HIL) simulations conducted on an embedded GPU platform (Nvidia Jetson Nano). Numerical results demonstrate that the proposed algorithm reduces the computation time of a high-performance trajectory tracking MPC to 2.98 milliseconds, thus providing a promising solution for real-time autonomous control of quadrotor UAVs on embedded systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Neural MPC for quadrotor trajectory tracking on embedded GPUs

  • 期刊论文

  • 发表日期：

    2026年2月19日

查看全文
  • Tianxun Li,
  • Keyou You

摘要

This paper investigates the computational challenges associated with Model Predictive Control (MPC) for trajectory tracking of quadrotor Unmanned Aerial Vehicles (UAVs). Despite its superior control performance, MPC is difficult to implement in real-time applications on resource-limited embedded systems due to its high computational complexity. Prior research has introduced explicit MPC methods to accelerate MPC computation, but these methods are generally ineffective in embedded applications involving complex systems, such as UAVs, which require high-frequency, real-time control. To overcome these limitations, this study proposes a novel MPC framework for quadrotor UAV trajectory tracking, specifically tailored for embedded GPUs. The method integrates Lyapunov Guidance Vectors (LGV) and Deep Neural Networks (DNN) to significantly reduce computation time. Our approach reduces the computation time to about one thirty-fifth of that required by conventional MPC methods, while still maintaining accurate trajectory tracking performance. Additionally, we validate the effectiveness of the proposed method through Hardware-in-the-Loop (HIL) simulations conducted on an embedded GPU platform (Nvidia Jetson Nano). Numerical results demonstrate that the proposed algorithm reduces the computation time of a high-performance trajectory tracking MPC to 2.98 milliseconds, thus providing a promising solution for real-time autonomous control of quadrotor UAVs on embedded systems.