用MPC实现四轴无人机路径跟踪，实时性更强且支持约束处理。
Model Predictive Path-Following Control for a Quadrotor
- 基于MPC构建级联控制结构，融合姿态控制器提升实时性能。
- 在Crazyflie硬件上验证，实现精确路径跟随并满足状态与输入约束。
- 新增走廊路径跟随机制，适应实际飞行中路径偏离需求。
自动化无人机辅助任务是一项复杂挑战。现有方案多依赖轨迹生成与跟踪，而路径跟随控制则提供了一种更直观自然的自动化方法。然而，多数现有方法无法显式处理状态与输入约束，采用保守的两阶段设计，或仅适用于线性系统。为此，本文基于模型预测控制（MPC）构建路径跟随框架，并首次将其应用于Crazyflie四轴无人机，通过真实硬件实验验证有效性。所提方法引入包含底层姿态控制器的级联控制结构，以应对四轴飞行器控制的高实时性要求。此外，为解决严格路径跟随可能过于苛刻的问题，还提出一种允许路径偏差的走廊路径跟随扩展方案。实验结果表明，该方法在真实场景中表现优异，是MPC路径跟随技术在四轴无人机上的首次成功应用。
原文摘要 · Abstract (English)
Automating drone-assisted processes is a complex task. Many solutions rely on trajectory generation and tracking, whereas in contrast, path-following control is a particularly promising approach, offering an intuitive and natural approach to automate tasks for drones and other vehicles. While different solutions to the path-following problem have been proposed, most of them lack the capability to explicitly handle state and input constraints, are formulated in a conservative two-stage approach, or are only applicable to linear systems. To address these challenges, the paper is built upon a Model Predictive Control-based path-following framework and extends its application to the Crazyflie quadrotor, which is investigated in hardware experiments. A cascaded control structure including an underlying attitude controller is included in the Model Predictive Path-Following Control formulation to meet the challenging real-time demands of quadrotor control. The effectiveness of the proposed method is demonstrated through real-world experiments, representing, to the best of the authors' knowledge, a novel application of this MPC-based path-following approach to the quadrotor. Additionally, as an extension to the original method, to allow for deviations of the path in cases where the precise following of the path might be overly restrictive, a corridor path-following approach is presented.
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