Intelligent System Control

Research Staff

  • Prof. Kenji Sugimoto

    Prof.
    Kenji Sugimoto

  • Assist.Prof. Taisuke Kobayashi

    Assist.Prof.
    Taisuke Kobayashi

  • Assist.Prof. Masaki Ogura

    Assist.Prof.
    Masaki Ogura

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

Research Areas

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 (Fig. 1).

Networked dynamical systems
The goal of this research is to provide a better understanding of dynamical processes taking place over complex networks, as well as developing effective strategies to control their behavior. Applications of this research direction can be found in a wide variety of contexts, from social networks to networked infrastructure and cyber-physical systems (Figs. 2 and 3).

Positive systems
Positive systems are dynamical systems whose response signals to nonnegative input signals are constrained to be nonnegative and have applications in pharmacology, epidemiology, population biology, multi-agent systems, and communication networks. We develop a novel framework toward the synthesis of positive systems based on geometric programming. Our application areas include product development processes, financial systems, data-center managements, and systems biology.

2. Machine learning for robotics

Biologically-inspired learning
We are studying new (reinforcement) learning structures inspired by animals, which would convert mathematically convenient ones into ones suitable for real robotic problems. For example, we are developing new neural network dynamics, reinforcement learning scheme, reward reshaping to be optimized, and so on (Fig. 4).

Physical human-robot interaction
We are developing for next-generation robots that can physically interact with human and aim to support various human motions: e.g., shared autonomy for autonomous driving car; adaptive robot control based on recognized human behaviors; multi-agent systems including human (Fig. 5).

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 Networked control system

Fig.1 Networked control system

Fig.2 Containment of epidemic spreading processes over complex networks

Fig.2 Containment of epidemic spreading processes over complex networks

Fig.3 Positive systems applications

Fig.3 Positive systems applications

Fig.4 Robot control by reinforcement learning

Fig.4 Robot control by reinforcement learning

Fig.5 Multi-agent system for physical human-robot interaction

Fig.5 Multi-agent system for physical human-robot interaction