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Applied Mathematical Systems Lab
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The lab explores control and data analysis through mathematical methods.
AboutAbout the lab
Exploring system modeling and control through mathematics.
The Applied Mathematical Systems Lab studies a wide range of systems mathematically, building on control theory, optimization theory, time series analysis, and machine learning.
In its research, the lab considers the essential characteristics of the target system and aims for an appropriate model and problem formulation.
Its goal is to build a unified theoretical framework for modeling and design toward robust cyber-physical systems, and to apply it to real systems.
The lab seminar consists of individual meetings to check research progress and reading-group sessions on literature in systems control, optimization, signal processing, and related fields.
Key terms
- Cyber-physical system
- A system that operates by combining real machines and devices with computation and data processing
- Robustness
- The property of continuing to function even when conditions change or errors occur
- Time series analysis
- A method for investigating the underlying rules and features of data from how it changes over time
- Mathematical optimization
- A mathematical method for finding the best choice under given conditions
NewsLab news
Lab introduction sessions held for the 2026 lab assignments (Oct. 15, 22, and 27)
Paper by a graduate and Prof. Kawabe published in the Transactions of the Institute of Systems, Control and Information Engineers
Paper by a master's student and Prof. Kawabe accepted by the Malaysian Journal of Student Advancement
A doctoral student received a Student Award (President's Award)
A master's student received the SICE Outstanding Student Award from the Society of Instrument and Control Engineers
ResearchResearch themes
Building on systems and control theory, optimization theory, signal processing, machine learning, and related fields, the lab studies theory and technology for modeling various systems, time series analysis, control system design, and decision-making. Its main research themes are distributed cooperative control, vehicle control, nonlinear time series analysis, and machine learning and optimization for decision-making.
*Illustrative imageRisk-Adaptive Distributed Cooperative Control
For control in which multiple agents cooperate in highly uncertain, high-risk environments, the lab aims to establish design principles based on the discovery of mathematical structures by AI. It incorporates mathematical constraints such as matroids into deep learning, aiming to realize safe reinforcement learning.
*Illustrative imageControl of Vehicle Systems
The lab studies control technologies that improve motion performance, safety, and ride comfort for automobiles, ships, aircraft, and vehicle-type mobile robots, as well as the automation of cooperative work by multiple mobile robots. It also covers vehicle platoon formation control that applies a model of bat flight.
*Illustrative imageTheory and Applications of Nonlinear Time Series Analysis
By deepening time series analysis and data analysis, the lab aims to bring about social change. Students are working on understanding and predicting large earthquakes, and on distinguishing patients with conditions such as Parkinson's disease from healthy people using gait data.
*Illustrative imageMachine Learning and Mathematical Optimization for Decision-Making
The lab develops methods that combine machine learning and mathematical optimization to optimize future decisions under uncertainty. To address the unavoidable problem of prediction error, it works on developing more robust prediction and optimization methods.
MembersFaculty
The members of the Applied Mathematical Systems Lab consist of faculty and students in the doctoral program, the master's program, and the undergraduate program.As of 2025
- ProfessorTohru Kawabe
- ProfessorYoshito Hirata
- Assistant ProfessorShunnosuke Ikeda
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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: “Wind turbine in full 01” Lauri Veerde / CC BY-SA 4.0 (cropped)
- Risk-Adaptive Distributed Cooperative Control: “Swarm of Colias Robot” Farshadarvin / CC BY-SA 4.0 (cropped)
- Control of Vehicle Systems: “Jungheinrich Autonomous Robot arculee M” Marc Auledas Noguera (Auledas) / CC BY 4.0 (cropped)
- Theory and Applications of Nonlinear Time Series Analysis: “Seismogram at Weston Observatory” Z22 / CC BY-SA 3.0 (cropped)
- Machine Learning and Mathematical Optimization for Decision-Making: “Aerial image of the Eurogate and Burchardkai container terminals (view from the southeast)” Carsten Steger / CC BY-SA 4.0 (cropped)
- Related lab (Mathematical Engineering Lab): “Robot Brain Surgeon GPN” NASA / Public domain (cropped)
- Related lab (Social Robot Lab): “Team ViGIR Robot Florian DARPA Robotics Challenge 2015” Sun L. Vega / Public domain (cropped)
- Related lab (Systems Mathematics Lab): “Shipping container - Port of Rotterdam - 2017 - 3” AgainErick / CC BY-SA 4.0 (cropped)
- Related lab (Chaos Lab): “Hyperkomplexe Mandelbrot-Menge HQ 20190908” PantheraLeo1359531 / CC BY-SA 4.0 (cropped)
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