Modeling and Control of a Coaxial Monocopter via Moving Mass Actuation

Aerial drone technology plays an increasingly significant role in modern life, with applications ranging from environmental monitoring and infrastructure inspection to delivery services and aerial photography. As the range of use cases continues to expand, there is a growing need to explore innovative drone types and configurations that can offer improved performance, enhanced efficiency, and adaptability to complex environments. Among these, the coaxial monocopter has emerged as a promising alternative to conventional quadrotors, offering aerodynamic efficiency and reduced mechanical complexity. This study presents the structural design, dynamic modeling, simulation, and control development of a novel moving-mass coaxial monocopter. The proposed platform utilizes internal mass displacement to achieve maneuverability, thereby eliminating the need for traditional active stabilization mechanisms. The design prioritizes simplicity and is tailored for long-endurance operation. The systemdynamics are derived using the Lagrangian method to accurately capture the coupled behavior introduced by the moving mass. A dynamic model is formulated to investigate stability conditions, and numerical simulations are conducted to validate the model and assess its nonlinear characteristics. A simple proportional-integral-derivative (PID) control strategy is implemented to address the primary control challenges, demonstrating the system’s ability to achieve stable flight. Comparative analysis with a thrust-vectoring configuration highlights the advantages of the proposed approach, offering a streamlined and effective alternative for aerial mobility.

Authors

Institutions

Publication Details

Published
2025-07-16
DOI
https://doi.org/10.2514/6.2025-3083
Citations
1
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
1.28
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Modeling and Control of a Coaxial Monocopter via Moving Mass Actuation

Mostafa Hassanalian, Dang Khai Nguyen, Samuel Maimako
1 citations
Robotic Path Planning Algorithms
1.28
article

Modeling and Control of a Coaxial Monocopter via Moving Mass Actuation

Mostafa Hassanalian, Dang Khai Nguyen, Samuel Maimako
article en
1 citations

Abstract

Aerial drone technology plays an increasingly significant role in modern life, with applications ranging from environmental monitoring and infrastructure inspection to delivery services and aerial photography. As the range of use cases continues to expand, there is a growing need to explore innovative drone types and configurations that can offer improved performance, enhanced efficiency, and adaptability to complex environments. Among these, the coaxial monocopter has emerged as a promising alternative to conventional quadrotors, offering aerodynamic efficiency and reduced mechanical complexity. This study presents the structural design, dynamic modeling, simulation, and control development of a novel moving-mass coaxial monocopter. The proposed platform utilizes internal mass displacement to achieve maneuverability, thereby eliminating the need for traditional active stabilization mechanisms. The design prioritizes simplicity and is tailored for long-endurance operation. The systemdynamics are derived using the Lagrangian method to accurately capture the coupled behavior introduced by the moving mass. A dynamic model is formulated to investigate stability conditions, and numerical simulations are conducted to validate the model and assess its nonlinear characteristics. A simple proportional-integral-derivative (PID) control strategy is implemented to address the primary control challenges, demonstrating the system’s ability to achieve stable flight. Comparative analysis with a thrust-vectoring configuration highlights the advantages of the proposed approach, offering a streamlined and effective alternative for aerial mobility.

New Mexico Institute of Mining and Technology (US)
Affordable and clean energy
Openalex Percentile: Top 18%
Robotic Path Planning Algorithms
1.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.