Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems

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 syste...

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Main Authors: Bryan S. Guevara, Jose Varela-Aldas, Viviana Moya, Manuel Cardona, Daniel C. Gandolfo, Juan M. Toibero
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/11049658/
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author Bryan S. Guevara
Jose Varela-Aldas
Viviana Moya
Manuel Cardona
Daniel C. Gandolfo
Juan M. Toibero
author_facet Bryan S. Guevara
Jose Varela-Aldas
Viviana Moya
Manuel Cardona
Daniel C. Gandolfo
Juan M. Toibero
author_sort Bryan S. Guevara
collection DOAJ
description 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 uncrewed 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 proposed method was experimentally validated using a DJI Matrice 100 quadrotor platform, considering scenarios with prior knowledge of obstacle locations. Results demonstrate the controller’s effectiveness in minimizing orthogonal and tangential tracking errors, ensuring stability and safety in complex environments.
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publishDate 2025-01-01
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spelling doaj-art-fd5ec2fd37534b70a2f3d8369e4f006f2025-08-20T02:38:22ZengIEEEIEEE Access2169-35362025-01-011310974210975110.1109/ACCESS.2025.358250511049658Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV SystemsBryan S. Guevara0https://orcid.org/0000-0002-7830-0498Jose Varela-Aldas1https://orcid.org/0000-0002-4084-1424Viviana Moya2https://orcid.org/0000-0002-6064-6925Manuel Cardona3https://orcid.org/0000-0002-4211-3498Daniel C. Gandolfo4https://orcid.org/0000-0002-4938-2105Juan M. Toibero5https://orcid.org/0000-0001-9347-566XInstituto de Automática, Universidad Nacional de San Juan-CONICET, San Juan, ArgentinaCentro de Investigación MIST, Facultad de Ingenierías, Universidad Tecnológica Indoamérica, Ambato, EcuadorFacultad de Ciencias Tècnicas, Universidad Internacional del Ecuador (UIDE), Quito, EcuadorResearch Department, Universidad Don Bosco, Soyapango, El SalvadorInstituto de Automática, Universidad Nacional de San Juan-CONICET, San Juan, ArgentinaInstituto de Automática, Universidad Nacional de San Juan-CONICET, San Juan, ArgentinaIn 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 uncrewed 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 proposed method was experimentally validated using a DJI Matrice 100 quadrotor platform, considering scenarios with prior knowledge of obstacle locations. Results demonstrate the controller’s effectiveness in minimizing orthogonal and tangential tracking errors, ensuring stability and safety in complex environments.https://ieeexplore.ieee.org/document/11049658/MPCCCLFCBFUAVobstacle avoidancepath-following
spellingShingle Bryan S. Guevara
Jose Varela-Aldas
Viviana Moya
Manuel Cardona
Daniel C. Gandolfo
Juan M. Toibero
Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
IEEE Access
MPCC
CLF
CBF
UAV
obstacle avoidance
path-following
title Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
title_full Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
title_fullStr Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
title_full_unstemmed Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
title_short Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
title_sort model predictive contouring control with barrier and lyapunov functions for stable path following in uav systems
topic MPCC
CLF
CBF
UAV
obstacle avoidance
path-following
url https://ieeexplore.ieee.org/document/11049658/
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AT vivianamoya modelpredictivecontouringcontrolwithbarrierandlyapunovfunctionsforstablepathfollowinginuavsystems
AT manuelcardona modelpredictivecontouringcontrolwithbarrierandlyapunovfunctionsforstablepathfollowinginuavsystems
AT danielcgandolfo modelpredictivecontouringcontrolwithbarrierandlyapunovfunctionsforstablepathfollowinginuavsystems
AT juanmtoibero modelpredictivecontouringcontrolwithbarrierandlyapunovfunctionsforstablepathfollowinginuavsystems