Varela Aldas, José
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Preferred name
Varela Aldas, José
Main Affiliation
Ambato
Email
josevarela@uti.edu.ec
ORCID
Scopus Author ID
57192546163

Research Output
Now showing 1 - 5 of 5
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Estimation of Unmodeled Dynamics: Nonlinear MPC and Adaptive Control Law With Momentum Observer Dynamic

2024 , Bryan S. Guevara , Luis F. Recalde , Viviana Moya , Varela Aldas, José , Daniel C. Gandolfo , Juan M. Toibero

This article proposes an enhancement to estimate unmodeled dynamics within the simplified dynamic model of a quadcopter by integrating three key methodologies: Nonlinear Model Predictive Control (NMPC), a Momentum Observer Dynamics (MOD), and an adaptive control law. Termed as Adaptive NMPC with MOD, this integrated approach leverages NMPC, implemented using the CasADi framework, for real-time decision-making, while the momentum observer facilitates system state estimation and uncertainty mitigation. Simultaneously, the adaptive control law adjusts parameters to estimate errors in unmodeled dynamics. Through digital twin and Model in Loop (MiL) simulations, the effectiveness of this framework is demonstrated. Specifically, the study focuses on the simplified quadcopter model, acknowledging often overlooked inherent dynamics resulting from the simplification by not considering the nonlinearities induced by the drone's attitude angles. Addressing these unmodeled dynamics is critical, and the Adaptive NMPC with MOD method emerges as a robust solution, showcasing its potential across various scenarios.

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Publication

Bridging Simulation and Reality: A Digital Twin Approach for UAVs

2025 , Bryan S. Guevara , Varela Aldas, José , Viviana Moya , Daniel C. Gandolfo , Juan M. Toibero

This study presents an innovative methodology for the development and testing of a digital twin for an Unmanned Aerial Vehicle (UAV), effectively bridging the simulation-reality gap. The proposed approach integrates Model-in-the-Loop (MiL) and Hardware-in-the-Loop (HiL) testing, enabling a comprehensive evaluation of the UAV’s digital twin in simulated environments. Behavioral testing includes open-loop scenarios, baseline feedback controllers, and Model Predictive Control (MPC). The UAV’s dynamic model is simplified and rigorously validated through experimental verification, ensuring high fidelity and reliability. Furthermore, this approach facilitates the critical transition from simulation to real-world experimentation by providing a robust framework for evaluating UAV performance under realistic conditions. This methodology highlights the importance of experimental validation in replicating real-world scenarios, ensuring the robustness and accuracy of the digital twin.

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Publication

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

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.

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Bilateral Rate/Position Delayed Teleoperation Control for UAVs: A Performance Evaluation

2025 , Chicaiza Claudio, Fernando , Emanuel Slawiñski , Viviana Moya , Carvajal, Christian , Varela Aldas, José , Ayala-Chauvin, Manuel Ignacio

This paper introduces a bilateral teleoperation system for UAVs that employs a hybrid control scheme combining rate and non-linear position modes. By continuously switching between these modes, the system achieves both agile manoeuvring and precise positioning under communication delays. Validation is carried out using a dynamic model for the master robot with a Novint Falcon haptic device and a simplified model for the slave robot in Gazebo-ROS2. Performance metrics including task completion time, mean squared error, and force feedback demonstrate enhanced stability and efficiency, suggesting promising applications in inspection, environmental monitoring and search and rescue

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Data-Driven Model Predictive Control for Trajectory Tracking in UAV-Manipulator Systems

2025 , Bryan S. Guevara , Varela Aldas, José , Viviana Moya , Manuel Cardona , Daniel C. Gandolfo , Juan M. Toibero

This work presents the design and implementation of a data-driven Nonlinear Model Predictive Control (NMPC) framework for an Uncrewed Aerial Vehicle (UAV) equipped with a 3-DOF robotic arm. Real-world data was collected using the Matrice 100 platform and Dynamixel MX-28AR actuators to identify a high-dimensional linear model via Dynamic Mode Decomposition with Control (DMDc), capturing the interactions between the aerial vehicle and the manipulator across 21 state variables. This DMDc-based model is embedded within the NMPC formulation to predict system behavior over finite horizons. The UAV’s orientation is represented using quaternions, enabling continuous and singularity-free attitude control. Additionally, the redundancy of the UAV-manipulator system allows for the integration of secondary objectives into the cost function, supporting flexible task execution. To meet real-time requirements, the control problem is solved using the Acados solver. The resulting controller achieves high-precision tracking while managing internal constraints, demonstrating the potential of data-driven NMPC in aerial manipulation tasks

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