JM. Castiblanco Quintero

Jose M. Castiblanco Quintero

I connect geometry-driven optimisation, co-simulation, and motion-capture validation to understand and improve UAV performance under high-dynamic operational constraints. Beyond performance improvement, my research investigates how optimisation, control, and data-driven methods can contribute to intelligent autonomous systems that learn from operational experience and support adaptive decision-making under realistic flight conditions.

Last updated: June 2026

Academic Background

Former Research Fellow, Cranfield University (UK), Associate Fellow of the Higher Education Academy (AFHEA)

Researcher in Flight Dynamics: Guidance, Control & Optimisation of Aerial Systems.

Latest Publication

Optimising Drone Airframes for Racing and Flight Dynamics Performance

The Aeronautical Journal, 2026

View article: https://doi.org/10.1017/aer.2026.10140

This work presents an integrated framework linking airframe geometry, nonlinear flight dynamics, and control synthesis for high-performance multirotor systems. The approach enables physically consistent optimisation under realistic flight conditions, allowing performance evaluation close to the vehicle's dynamic limits.

Technical implementation

Public baseline of the Geometry-Aware Guidance Control platform.

View GitHub repository: https://github.com/Jocasqui/geometry-aware-guidance-control

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Flight dynamics and control of multirotor aerial vehicles operating near physical and control limits, with emphasis on geometric design, nonlinear dynamics, and performance-driven optimisation.

Research Vision

My research addresses fundamental questions in the flight dynamics and control of multirotor aerial vehicles operating near physical and control limits. I investigate how airframe geometry, mass distribution, and actuator layout shape the nonlinear system behaviour of aerial platforms required to operate under demanding operational conditions.

A central contribution of my work is the explicit integration of geometric design variables into the modelling, control, and optimisation loop. By treating geometry as an active component of the dynamical system, I enable the systematic analysis of design–control–performance couplings that govern stability, robustness, and efficiency.

Methodologically, my research combines experimentally validated nonlinear models, implicit numerical formulations, and multi-objective optimisation strategies with modern control architectures. Beyond specific vehicle classes or mission profiles, these contributions inform the analysis and control of complex, highly coupled dynamical systems operating at the limits of achievable performance.

Applied GNC & Autonomy (capability-focused)

Guidance & path-following: LOS/look-ahead with Speed planning, trajectory shaping under acceleration/tilt limits
Control: cascaded attitude/rate control, saturation-aware tuning, performance metrics
Estimation (GPS-denied ready / Vicon system): Extended-KF / Unscented-KF / Visual-Inertial Odometry / SLAM familiarity + active implementation
Validation: SIL/HIL mindset + log/metrics pipeline + motion-capture ground truth

Key Research Themes

• High-Performance Flight Dynamics

Nonlinear modelling and analysis of multirotor aerial vehicles operating near physical, energetic, and control limits.

• Geometry-Aware Modelling and Control

Explicit integration of airframe geometry, mass distribution, and actuator layout into modelling, control, and optimisation pipelines.

• Guidance, Navigation & Control

Arc-line trajectory modelling and speed planning integrated with guidance and control for high-performance aerial vehicles operating near physical limits.

• Experimental Validation and Benchmarking

Motion-capture-based flight testing and reproducible experimental frameworks for validating high-performance aerial systems.

• Design–Control Coupling and Optimisation

Multi-objective optimisation of aerial platforms accounting for dynamic performance, robustness, and efficiency under realistic operational constraints.

Publications & Preprints

Selected Journal Publications

• Castiblanco Quintero, J. M., et al.(2026). Optimising Drone Airframes for Racing and Flight Dynamics Performance. The Aeronautical Journal | Cambridge Core.

• Castiblanco Quintero, J. M., et al.(2024). Improving Racing Drones Flight Analysis: A Data-Driven Approach Using Motion Capture Systems. MDPI - Drones.

• Castiblanco Quintero, J. M., et al. (2022). Co-simulation platform for geometric design, trajectory control, and guidance of racing drones. Sage Journals | International Journal of Micro Air Vehicles.

Experimental and Computational Infrastructure

My research is supported by an integrated experimental and computational infrastructure designed to connect geometric design, nonlinear modelling, control synthesis, and flight validation within a single workflow.

Experimentally, I employ motion-capture-based flight arenas and instrumented multirotor platforms to obtain high-fidelity datasets under controlled and repeatable conditions. These facilities enable systematic validation of dynamic models, control strategies, and performance metrics in regimes close to operational limits.

Computationally, I develop physics-based nonlinear models, co-simulation environments, and optimisation-driven design pipelines that explicitly propagate geometric and physical parameters into system-level dynamics and control performance. This combination supports reproducible analysis, rapid prototyping, and principled exploration of design–control trade-offs.

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