Options
Varela Aldas, José
Model Predictive Contouring Control With Barrier and Lyapunov Functions for Stable Path-Following in UAV Systems
SINDy and PD-Based UAV Dynamics Identification for MPC
2025
,
Bryan S. Guevara
,
Varela Aldas, José
,
Viviana Moya
,
Manuel Cardona
,
Daniel C. Gandolfo
,
Juan M. Toibero
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.
2025
,
Bryan S. Guevara
,
Varela Aldas, José
,
Daniel C. Gandolfo
,
Juan M. Toibero
This study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proportional-Derivative (PD)-based control input approximation and Sparse Identification of Nonlinear Dynamics (SINDy). Addressing the inherent platform constraints—where control inputs are restricted to specific attitude angles and z-axis velocities—thrust and torque are approximated via a PD controller, which serves as a practical intermediary for facilitating nonlinear system identification. Both methodologies leverage data-driven strategies to construct compact and interpretable models from experimental data, capturing significant nonlinearities with high fidelity. The resulting models are rigorously evaluated within a Model Predictive Control (MPC) framework, demonstrating their efficacy in precise trajectory tracking. Furthermore, the integration of data-driven insights enhances the accuracy of the identified models and improves control performance. This framework offers a robust and adaptable solution for analyzing UAV dynamics under realistic operational conditions, emphasizing the comparative strengths and applicability of each modeling approach.