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.
@article{arxiv.2411.00668,
title = {Model Predictive Contouring Control with Barrier and Lyapunov Functions for Stable Path-Following in UAV systems},
author = {Bryan S. Guevara and Viviana Moya and Luis F. Recalde and David Pozo-Espin and Daniel C. Gandolfo and Juan M. Toibero},
journal= {arXiv preprint arXiv:2411.00668},
year = {2024}
}