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Control Engineering Lab
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The lab builds theory to make everything from drones to satellites move as intended.
AboutAbout the lab
Building theory to make things move as intended.
The Control Engineering Lab studies system control theory for handling aerospace systems.
Its themes include nonlinear control, optimal control, stochastic systems, model order reduction, robotics, and machine learning.
The lab aims to develop mechanics-based modeling and control methods for large-scale aerospace systems.
It pursues its research through various approaches, including devising theory, numerical simulation, and experiments on real hardware.
Key terms
- Optimal control
- Control that determines inputs so as to best satisfy a criterion, such as reaching a goal quickly or reducing energy use
- System identification
- Building a mathematical model that describes the behavior of a target system from data measured on it
- Port-Hamiltonian system
- A mathematical form that represents many physical systems using the flow of energy in and out
NewsLab news
A lab member received the Young Researchers' Outstanding Presentation Fellow Award from the Japan Society of Mechanical Engineers
A lab member received the 2026 Encouragement Award from the Institute of Systems, Control and Information Engineers (ISCIE)
A lab member received the SCI Student Presentation Award at the 70th ISCIE Conference on Research Presentations
ResearchResearch themes
The lab develops system control theory for handling a wide range of systems, not only aerospace but also mechanical, electrical, physical, chemical, information, and social systems. Recently, it has focused on nonlinear control based on analytical mechanics and on database-driven nonlinear optimal control.
*Illustrative imageControlling Through the Lens of Energy
In port-Hamiltonian systems, which can represent many physical systems, the energy of the system can be used to judge stability. The lab develops stable and robust nonlinear controllers by designing artificial potential functions.
*Illustrative imageOptimization That Minimizes Fuel
For spacecraft that are difficult to refuel, the lab studies various mathematical optimization algorithms, such as methods for solving L1 optimal control problems that find trajectories with minimum fuel consumption, along with the theory of their convergence.
*Illustrative imageBuilding System Models with Machine Learning
The lab proposes methods that identify complex nonlinear systems from data using neural networks (system identification). It is trying to apply them to systems involving complex physical phenomena and to autonomous driving of cars.
*Illustrative imageOptimal Feedback Control from Data
For optimal feedback control of nonlinear systems, the lab proposes a method that determines the control from a database of stored optimal solutions instead of solving the equations directly. Its effectiveness has been verified on real drones.
MembersFaculty
The members of the Control Engineering Lab consist of faculty, doctoral students, master's students, and undergraduates.
- ProfessorKenji Fujimoto
- Associate ProfessorIchiro Maruta
- Assistant ProfessorKana Shikada
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Quadcopter Drone in flight” Project Kei / CC BY-SA 4.0 (cropped)
- Controlling Through the Lens of Energy: “The Cygnus XL cargo spacecraft is in the grasp of the Canadarm2 robotic arm (iss074e0378294)” NASA Johnson Space Center / Public domain (cropped)
- Optimization That Minimizes Fuel: “Launch of H-IIA Rocket Flight No. 47” G4qg / CC BY-SA 4.0 (cropped)
- Building System Models with Machine Learning: “Simplified neural network example” Mikael Häggström, M.D. Author info - Reusing images- Conflicts of interest: None Mikael Häggström, M.D. / CC0 (cropped)
- Optimal Feedback Control from Data: “Industrial PID controllers - front display” Bitjungle / CC BY-SA 4.0 (cropped)
- Related lab (Propulsion Engineering Lab): “Xenon hall thruster” Jet Propulsion Laboratory / Public domain (cropped)
- Related lab (Aerospace Dynamics Lab): “Common mime (Papilio clytia) form dissimilis in flight Luang Prabang” Charles J. Sharp / CC BY-SA 4.0 (cropped)
- Related lab (Neutron Engineering Lab): “Fields and neutron source - geograph.org.uk - 5301501” Rudi Winter / CC BY-SA 2.0 (cropped)
- Related lab (Fluid Dynamics Lab): “Shockwave pattern around a T-38C observed with Background-Oriented Schlieren photography (1)” NASA & US Air Force: J.T. Heineck / Ed Schairer / Maj. Jonathan Orso / Maj. Jeremy Vanderhal / Public domain (cropped)
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