Aerial transportation using quadrotors with cable-suspended payloads holds great potential for applications in disaster response, logistics, and infrastructure maintenance. However, their hybrid and underactuated dynamics pose significant control and perception challenges. Traditional approaches often assume a taut cable condition, limiting their effectiveness in real-world applications where slack-to-taut transitions occur due to disturbances. We introduce ES-HPC-MPC, a model predictive control framework that enforces exponential stability and perception-constrained control under hybrid dynamics. Our method leverages Exponentially Stabilizing Control Lyapunov Functions (ES-CLFs) to enforce stability during the tasks and Control Barrier Functions (CBFs) to maintain the payload within the onboard camera's field of view (FoV). We validate our method through both simulation and real-world experiments, demonstrating stable trajectory tracking and reliable payload perception. We validate that our method maintains stability and satisfies perception constraints while tracking dynamically infeasible trajectories and when the system is subjected to hybrid mode transitions caused by unexpected disturbances.

翻译：利用带缆索悬挂负载的四旋翼进行空中运输在灾害响应、物流和基础设施维护等领域具有巨大应用潜力。然而，其混合欠驱动动力学特性带来了显著的控制与感知挑战。传统方法通常假设缆索处于张紧状态，这限制了其在现实应用中的有效性，因为扰动会导致缆索在松弛与张紧状态间切换。本文提出ES-HPC-MPC，一种在混合动力学下强制执行指数稳定性与感知约束控制的模型预测控制框架。该方法利用指数稳定控制李雅普诺夫函数（ES-CLFs）确保任务执行期间的稳定性，并采用控制屏障函数（CBFs）将负载保持在机载摄像头的视场（FoV）内。我们通过仿真和真实世界实验验证了该方法，证明了其稳定的轨迹跟踪能力和可靠的负载感知性能。实验验证表明，在跟踪动态不可行轨迹以及系统因意外扰动发生混合模式切换时，本方法仍能保持稳定性并满足感知约束。