Intelligent System Control

Research Staff

  • Prof. Kenji Sugimoto

    Prof.
    Kenji Sugimoto

  • Assoc.Prof. Takamitsu Matsubara

    Assoc.Prof.
    Takamitsu Matsubara

  • Affiliate Assoc.Prof. Yuki Minami

    Affiliate Assoc.Prof.
    Yuki Minami

  • Assist.Prof. Taisuke Kobayashi

    Assist.Prof.
    Taisuke Kobayashi

  • Assist.Prof. Masaki Ogura

    Assist.Prof.
    Masaki Ogura

E-mail { kenji, takam-m, minami, kobayashi, oguram }[at] is.naist.jp

Research Area

1. Control systems design

Advanced robust/adaptive control
We study advanced theories in post-modern robust/adaptive control and their applications including current investigations into various schemes of feedforward learning control (feedback error learning). System identification and state estimation are also topics of interest. We also investigate control theory for quantized systems whose input is restricted to discrete-valued signals.

Distributed control
We conduct theoretical and experimental studies on distributed LED lighting systems. We also study power demand-supply balance control for distributed generation network systems composed of multiple generators such as gas-engine and photovoltaic generators.

2. Machine learning for robotics

Motor skill learning for humanoid robots
We are developing novel methods that enable robots to learn complex motor skills (e.g., biped walking, putting on T-shirts and clothing assistance) by optimal control and reinforcement learning.

Constructing practical myoelectric interfaces for robot control
We construct myoelectric interfaces robust to postural changes, sweating, and muscular fatigue, using surface electromyograms (sEMG) via modern machine learning methods.

Key Features

We welcome motivated students from various fields including mechanical/electrical engineering, mathematical/physical science, as well as computer science. The faculty guides students individually, taking into account their backgrounds, and assists them in mastering mathematical system approaches by the end of their course. Thereby they acquire a wide range of technical skills from fundamental theories to applications. The students in our lab are highly motivated, diligent, cooperative and eager to learn from others. We anxiously await such students from all over the world.

Fig.1 Feedback error learning control

Fig.1 Feedback error learning control

Fig.2 Quantized control of mechanical system

Fig.2 Quantized control of mechanical system

Fig.3 System Identification by manifold learning

Fig.3 System identification by manifold learning

Fig.4 Motor skill learning by enforcement learning

Fig.4 Motor skill learning by enforcement learning

Fig.5 Distributed lighting system and distributed generation network
system

Fig.5 Distributed lighting system and distributed generation network system