Real-world systems are inherently nonlinear, yet in engineering they are often treated as linear systems for simplicity. By focusing on nonlinearity, we can uncover and realize behaviors and properties that cannot be explained by linear theory. In our laboratory, we study system nonlinearity from both theoretical and experimental perspectives, combining analytical methods and numerical simulations with hardware implementations such as electrical circuits. Currently, our research topics include artificial neurons, pedestrian synchronization on bridges, synchronization control of walking robots, and AI-based system identification and prediction.

Experiment with a walking robot