Jose M. Castiblanco Quintero
End-to-end research across flight dynamics, control, and geometry-driven optimisation for high-performance aerial systems.
End-to-end research across flight dynamics, control, and geometry-driven optimisation for high-performance aerial systems.
Jul 2018 – 2022.
This role grounded my research profile in hands-on experimentation and validation. I worked on drone racing and UAV projects (DRONE UPV, Umilles, IDL), collaborating with industrial groups on real challenges. I launched and managed laboratory experiments to validate aerodynamic designs, bridging CAD/CFD workflows with real tests, and supervised theses and final-year projects on drone dynamics, optimisation, and control.
Key contributions:
Drone racing / UAV projects with industry-facing challenges (DRONE UPV, Umilles, IDL).
Laboratory experimentation to validate aerodynamic designs, integrating CAD/CFD with real testing.
Supervision of theses and final-year projects focused on drone dynamics, optimisation, and control.
Bridge: This experimental foundation later became the basis for my co-simulation and data-centric workflows during my doctoral research.
August 2022 – 2023
My research focused on applying hybrid optimisation methods to flight dynamics, combining classical PID control with data-driven learning strategies to accommodate diverse computational platforms—from high-performance simulation environments to embedded systems. I developed co-simulation tools in MATLAB/Simulink and CAx software for full airframe analysis and control-loop design, enhanced by real-time sensor data integration with Vicon, MySQL, and ZeroMQ. These capabilities were applied within ongoing research projects at Cranfield University in collaboration with institutional partners.
Research outputs:
Co-simulation platform for geometric design, trajectory control, and guidance of racing drones (IJMAV, 2022): modular simulation environment using hybrid control strategies for system identification, controller tuning, and waypoint tracking.
Experimental study on the dynamic behavior of drones designed for racing competitions (IJMAV, 2021): geometry-driven motion behaviour using a rich dataset captured with a customised sensor environment and skilled FPV pilot inputs.
Hybrid dynamic algorithms and motion behaviour for optimising racing-drone airframes and trajectories, including an article in MDPI Drones (2025) and a TechRxiv preprint accepted in The Aeronautical Journal (2026, forthcoming).
Bridge: This stage consolidated the “model ↔ data ↔ validation” loop and prepared the systems-integration skills required for connected autonomy projects.
Sep 2023 - 2025.
I contributed to projects addressing real-world challenges in UAV autonomy, guidance, and control. The role expanded my experience in designing complex, connected systems that integrate multiple data sources and sensing platforms, and reinforced my expertise in flight dynamics and control theory—particularly in combining domain knowledge with machine learning and AI-based methods deployable on electronic subsystems with limited computing capacity in dynamic, constrained environments, in a safe manner. I also provided technical leadership on the connectivity and integration of software systems across flight controllers, ground stations with motion-tracker systems, ROS environments, and communication protocols, ensuring real-time interoperability and performance in autonomous aerial and air–sea applications.
Key contributions:
Technical leadership on connectivity and software integration across flight controllers, motion-tracker ground stations, ROS environments, and communication protocols.
Real-time interoperability and performance across heterogeneous platforms, multiple data sources, and sensing modalities.
Contributions to autonomy, guidance, and control developments under real-world constraints in multi-sensor settings.
Closing line: Together, these appointments reflect a continuous trajectory from experimental validation, through co-simulation and optimisation workflows, to real-time connected autonomy and control.
Across my appointments, I have built a continuous workflow from experimental validation and benchmarking to co-simulation toolchains and geometry-driven performance optimisation.
Context
High-performance UAVs require flight-dynamics insight under track-relevant constraints, where measurement fidelity and real-world disturbances shape what can be reliably modelled and controlled.
My role
Developed and applied motion-capture-driven analysis workflows to refine flight-dynamics understanding and extract performance insight from experimental data.
Technical backbone:
Motion-capture experimental workflow and dataset management.
Data pipelines for cleaning, fitting, and performance insight extraction,
Experiment ↔ model ↔ validation loop for hypothesis testing under operational constraints.
Outputs
Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems (2024).
Tags: Flight dynamics • Motion capture • Experimental validation • Data pipelines.
Context
Data-reproducible benchmarking is essential for comparing sensor configurations, analysing dynamic motion performance, and calibrating evidence-based modelling and control development.
My role
Created and curated an open dataset of indoor flight trials coupled with motion-capture ground truth, enabling repeatable analysis and validation workflows.
Technical backbone:
Structured data organisation for repeatability and downstream modelling.
Motion-capture measurement for high-fidelity kinematic ground truth.
Dataset design oriented to experimental comparison and calibration standards.
Outputs
TRAM-FPV Racing Open Database (dataset), Cranfield University’s Flight Arena (2023).
Tags: Open dataset • Benchmarking • Motion capture • Reproducibility.
Context
Geometry changes influence both dynamics and control authority, so design evaluation benefits from integrated environments that support identification, tuning, and trajectory-level tasks.
My role
Developed a modular co-simulation environment combining modelling, control-loop design, and guidance to support design and validation workflows.
Technical backbone:
MATLAB/Simulink co-simulation toolchain.
CAx-based full airframe analysis feeding control-loop design.
Hybrid control strategies supporting system identification, tuning, and waypoint tracking
Outputs
Co-Simulation Platform for Geometric Design, Trajectory Control and Guidance (2022).
Tags: Co-simulation • Guidance & control • Geometry-aware analysis • Tool development
Context
Optimising airframes for racing performance requires a systematic approach to balancing flight-dynamic trade-offs under demanding operations, realistic constraints, and measurable flight metrics.
My role
Contributed to research on airframe optimisation and performance extraction, anchored in experimentally grounded modelling workflows.
Technical backbone:
Model-informed performance metrics anchored in experiments.
Iterative computation design loop: geometry ↔ dynamics ↔ validation.
Focus on performance insight rather than purely geometric outcomes.
Outputs
Optimising Drone Airframes for Racing and Flight Dynamics Performance (2026 - In production).
Tags: Optimisation • Geometry-performance trade-offs • Flight dynamics • Experimental grounding.
I build end-to-end research workflows that connect modelling, control design, experimental benchmarking, and real-time system integration—so results remain reproducible and deployable in constrained environments.
Modelling & co-simulation • Guidance & control • Data-driven validation • Real-time interoperability • Embedded deployment.
I collaborate with academic and industrial partners to deliver research that is measurable, reproducible, and deployable—bridging modelling, sensing, and real-time connected autonomy.
Cross-institution projects linking experimental benchmarking, co-simulation toolchains, and system integration across heterogeneous UAV platforms.
Partnership-ready workflows with clear interfaces between flight controllers, ground stations, sensing stacks, and communication layers for real-time operation.
Led integration decisions across software systems to ensure interoperability, performance, and reliability in dynamic and constrained environments.
Translate domain knowledge into implementable control/AI solutions that remain safe and practical on subsystems with limited computing capacity.
Rapid prototyping and integration of connected autonomy pipelines (data → model → control → validation).
Evidence-driven evaluation using experimental benchmarks and dataset-oriented validation.
Clear documentation and modular components to enable adoption by multi-team consortia.
Optimising Drone Airframes for Racing and Flight Dynamics Performance — accepted in The Aeronautical Journal. (2025-2026)
Visual Servoing Model Predictive Control for Autonomous Shipboard Rotorcraft Landing in High Sea States - AIAA (2024-2025)