LUD: 2024.09.05
We are the Uchiyama and Masuda Laboratory in the Department of Aerospace Engineering, College of Science and Technology at Nihon University. Our research primarily focuses on aerospace control, spanning from attitude control of drones and planetary exploration rovers to satellite systems and trajectory design.
Systems and Control
We design autonomous control systems for drones, planetary exploration rovers, and other UAVs. Our work includes the development of small UAVs weighing less than 1 kg and specialized Quad-Tilt Wing UAVs (QTW-UAVs). Additionally, we research systems for safe flight that can avoid dynamic obstacles. Furthermore, we are proposing a new attitude control method for small satellites using spherical wheels, aiming to create a lightweight and practical attitude control system.
Trajectory optimization with collision avoidance
(Proposed by Shoshi Kawarabayashi in 2024)
| Novel control theory
We research a wide range of control theory to achieve sophisticated control of aerospace systems. Our recent research focuses on leveraging advanced control methodologies, such as traditional Linear Quadratic Regulator (LQR) controllers and sliding mode controllers, to enhance the control capabilities of Unmanned Aerial Vehicles (UAVs).
Trajectory tracking control
(Proposed by Shunsuke Komiyama in 2024)
Formation flight
(Proposed by Tsukasa Kaji in 2024)
| Multi-Vehicle Systems
Our research spans beyond attitude control to include the development of formation flight algorithms for UAVs and cooperative algorithms for UAVs interacting with UGVs. Additionally, we explore control theory tailored for multi-aircraft cargo transportation and other practical applications with future implications.
| Machine learning
We integrate machine learning into control engineering, harnessing its powerful capabilities. Our research incorporates advanced algorithms like deep neural networks and genetic algorithms to enhance control technology. Moreover, we explore cutting-edge control algorithms, including data-driven control theory leveraging big data, to push the boundaries of control engineering.
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Trajectory Design
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