标题： 带有障碍函数和李雅普诺夫函数的无人机路径跟随稳定模型预测轮廓控制 显示英文标题

标题： Model Predictive Contouring Control with Barrier and Lyapunov Functions for Stable Path-Following in UAV systems

摘要： 本研究提出了一种新颖的方法，将非线性模型预测轮廓控制（NMPCC）与指数镇定控制李雅普诺夫函数（ES-CLF）以及指数高阶控制屏障函数相结合，以实现无人机系统中的稳定路径跟随和障碍物规避。该框架使无人飞行器（UAV）能够在严格遵循期望路径的同时安全地绕过静态和动态障碍物。基于四元数的公式确保了精确的方向和姿态控制，而强大的优化求解器则强制执行由控制李雅普诺夫函数（CLF）和控制屏障函数（CBF）施加的约束，从而保证可靠的实际运行性能。该方法在模型在回路（MiL）环境中得到了验证，展示了有效的路径跟踪和障碍物规避能力。结果显示，该框架能够最小化正交和切向误差，确保复杂环境下的稳定性和安全性。 显示英文摘要

摘要： In this study, we propose a novel method that integrates Nonlinear Model Predictive Contour Control (NMPCC) with an Exponentially Stabilizing Control Lyapunov Function (ES-CLF) and Exponential Higher-Order Control Barrier Functions to achieve stable path-following and obstacle avoidance in UAV systems. This framework enables unmanned aerial vehicles (UAVs) to safely navigate around both static and dynamic obstacles while strictly adhering to desired paths. The quaternion-based formulation ensures precise orientation and attitude control, while a robust optimization solver enforces the constraints imposed by the Control Lyapunov Function (CLF) and Control Barrier Functions (CBF), ensuring reliable real-time performance. The method was validated in a Model-in-the-Loop (MiL) environment, demonstrating effective path tracking and obstacle avoidance. The results highlight the framework's ability to minimize both orthogonal and tangential errors, ensuring stability and safety in complex environments.