Control Synthesis
Explore AI-based methods for obtaining control strategies suited to quadrotor dynamics.
Autonomous aerial systems
Intelligent Control Synthesis for Quadrotor Systems
Design and computational evaluation of AI-based control strategies for autonomous aerial vehicles operating under dynamic conditions and disturbances.
Control · Dynamics · Simulation
Project overview
Autonomy through intelligent flight control
This project investigates methods for obtaining and evaluating control strategies for quadrotor systems by integrating flight-dynamics modeling, simulation and artificial intelligence techniques. The focus is on autonomous control behavior and systematic computational assessment under representative operating conditions.
Technical objectives
Core workstreams
A structured framework for studying the synthesis, response and robustness of intelligent quadrotor control strategies.
Simulation
Use computational environments to represent vehicle response and operational scenarios.
Disturbance Analysis
Assess controller behavior under perturbations and variations in operating conditions.
Evaluation Framework
Organize metrics and experimental evidence to characterize control performance.
Technical domains
Engineering and artificial intelligence
Bruno F. Barra Atarama
Aerospace engineer and master's student in Artificial Intelligence, with interests in autonomous systems, computational modeling and intelligent control.
Aerospace Engineer · Universidad Nacional de La Plata (UNLP)
Master's student in Artificial Intelligence · Universidad de San Andrés (UdeSA)