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

TL;DR

This paper introduces a new control framework combining model predictive control, Lyapunov functions, and barrier functions to enable UAVs to follow paths accurately while avoiding obstacles in real-time.

Contribution

It presents a novel integration of NMPCC with ES-CLF and CBFs for stable, obstacle-avoiding UAV path-following, validated in a simulation environment.

Findings

Effective path tracking with minimized errors

Reliable obstacle avoidance in static and dynamic environments

Stable real-time performance demonstrated in simulations

Abstract

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.