About

I am a Ph.D. student at Kyushu University, Joint Graduate School of Mathematics for Innovation. Currently, I work at the Control & Optimization Laboratory, advised by Prof. Kaoru Yamamoto.

Education Link to heading

  • 2024.04 - Present
    Ph.D. Student
    Joint Graduate School of Mathematics for Innovation, Kyushu University

  • 2022.04 - 2024.03
    Master of Engineering
    Graduate School of Environmental Engineering, The University of Kitakyushu

    • System Control Research Group, Ikeda Lab.
    • Supervisor: Prof. Takuya Ikeda
    • Thesis: Topology Identification for Consensus Network Systems via Sparse Structure Learning

  • 2018.04 - 2022.03
    Bachelor of Engineering
    Faculty of Environmental Engineering, The University of Kitakyushu

    • System Control Research Group, Ikeda Lab.
    • Supervisor: Prof. Takuya Ikeda
    • Thesis: Distributed Formation Control for Discrete-time Systems with Field-of-View Constraint

Outreach Activities Link to heading

  • 2025.04 - Present
    Student Representative, Technical Committee on Robot Control (IEEE Robotics and Automation Society)

Societies Link to heading

  • The Society of Instrument and Control Engineers (SICE)
  • The Institute of Systems, Control and Information Engineer (ISCIE)
  • The Robotics Society of Japan (RSJ)
  • The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
    • IEEE Control Systems Society (CSS)
    • IEEE Robotics and Automation Society (RAS)

Research Topic Link to heading

Sampled-data Systems Control for Real-world Agent Link to heading

In general, robots and drones acquire state variables such as position, velocity, and acceleration using sensors like cameras, laser rangefinders (LRFs), and accelerometers. Control inputs are then computed by an onboard digital computer. Therefore, the actual system is a mix of continuous-time and discrete-time systems—in other words, it belongs to the class of sampled-data control systems.
In this study, we investigate a control method capable of achieving stable control even in systems with slow sampling periods or in situations where information between sample points is lost, by applying a sampled-data control technique called “lifting” to real-world hardware.

Topology Identification for Networked Control Systems (Past) Link to heading

Cooperative control of robots, attitude synchronization of satellites, and vehicle rendezvous are all related to various challenges in networked control systems, and the control performance of such systems is greatly influenced by the “network topology” that represents the connectivity structure. Therefore, understanding the topology is critically important in order to maintain performance metrics such as task completion speed and connectivity.
In this study, we propose and evaluate a framework for accurately estimating the network topology based on the state values of nodes obtained from consensus network systems.