Inoue, Masaki

写真a

Affiliation

Faculty of Science and Technology, Department of Applied Physics and Physico-Informatics (Yagami)

Position

Associate Professor

Related Websites

Profile 【 Display / hide

  • April, 2004--March, 2007: Division of Mechanical, Materials and Manufacturing Science, School of Engineering, Osaka University (skip 4th year grade) April, 2007--March, 2009: Masters Course, Department of Mechanical Engineering, Graduate School of Engineering, Osaka University April, 2009--March, 2012: Doctors Course, Department of Mechanical Engineering, Graduate School of Engineering, Osaka University April, 2010--March, 2012: Japan Society for the Promotion of Science (DC2) April, 2012--March, 2014: FIRST, Aihara Innovative Mathematical Modelling Project, Japan Science and Technology Agency April, 2012--March, 2014: Department of Mechanical and Environmental Informatics, Graduate School of Information Science and Engineering, Tokyo Institute of Technology April, 2014--March, 2014: Assistant Professor, Department of Applied Physics and Physico-Informatic, Faculty of Science and Technology, Keio University April, 2021-- current: Associate Professor.

Profile Summary 【 Display / hide

  • システム制御理論の研究者として,人と機械の協調制御のための基礎理論構築から運転アシスト制御,農業環境制御,航空管制制御への応用展開まで取り組んでいます。

Career 【 Display / hide

  • 2009.04
    -
    2010.03

    大阪大学, 大学院 工学研究科 機械工学専攻, リサーチアシスタント(RA)

  • 2010.04
    -
    2012.03

    日本学術振興会, 特別研究員(DC2)

  • 2011.04
    -
    2012.03

    大阪大学, 大学院 工学研究科 機械工学専攻, シニアティーチングアシスタント(STA)

  • 2012.04
    -
    2014.03

    科学技術振興機構, FIRST 合原最先端数理モデルプロジェクト, 研究員

  • 2012.04
    -
    2014.03

    東京工業大学, 大学院 情報理工学研究科 情報環境学専攻, 研究員

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Academic Background 【 Display / hide

  • 2003.04
    -
    2007.03

    Osaka University, 工学部, 応用理工学科

    University, Skipped grade(s)

  • 2007.04
    -
    2009.03

    Osaka University, 工学研究科, 機械工学専攻

    Graduate School, Completed, Master's course

  • 2009.04
    -
    2012.03

    Osaka University, 工学研究科, 機械工学専攻

    Graduate School, Completed, Doctoral course

Academic Degrees 【 Display / hide

  • 博士(工学), 大阪大学, Coursework, 2012

 

Research Areas 【 Display / hide

  • Informatics / Mechanics and mechatronics

  • Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Control and system engineering

Research Keywords 【 Display / hide

  • control engineering

  • Human-in-the-loop Control Systems

  • 農業環境制御

  • air traffic management

  • smart grid

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Research Themes 【 Display / hide

  • 人と機械の協調制御, 

    2019.04
    -
    Present

  • 農業環境制御, 

    2020.04
    -
    Present

  • ドライバーの運転アシスト, 

    2020.04
    -
    Present

  • Air Traffic Management, 

    2018
    -
    Present

  • 大規模複雑システムの制御, 

    2017
    -
    Present

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Proposed Theme of Joint Research 【 Display / hide

  • Human-in-the-loop Control

    Desired form: Technical Consultation, Funded Research, Cooperative Research, Other

  • 人間行動のデータ駆動モデリング

 

Books 【 Display / hide

  • 次世代電力システム設計論-再生可能エネルギーを活かす予測と制御の調和-

    井上正樹,(井村順一・原辰次編著), オーム社, 2019.11,  Page: 423

    Scope: 7.4章「人と調和する制御:集合値信号を用いた階層化制御」,  Contact page: pp.344-351

  • Analysis and Control of Complex Dynamical Systems: Robust Bifurcation, Dynamic Attractors, and Network Complexity

    INOUE Masaki, IMURA Jun-ichi, KASHIMA Kenji, and AIHARA Kazuyuki, Springer, 2015

    Scope: Part I, Chapter 1, pp. 3-19 (Dynamic Robust Bifurcation Analysis)

Papers 【 Display / hide

  • Modularity in design of dynamical network systems: Retrofit control approach

    Ishizaki T., Sasahara H., Inoue M., Kawaguchi T., Imura J.I.

    IEEE Transactions on Automatic Control (IEEE Transactions on Automatic Control)  66 ( 11 ) 5205 - 5220 2021.11

    ISSN  00189286

     View Summary

    In this article, we develop a modular design method of decentralized controllers for linear dynamical network systems, where multiple subcontroller designers aim at individually regulating their local control performance with accessibility only to their respective subsystem models. First, we derive a constrained version of the Youla parameterization that characterizes all retrofit controllers for a single subcontroller, defined as an add-on-type subcontroller that manages a subsystem. The resultant feedback system is kept robustly stable for any variation in the neighboring subsystems, other than the subsystem of interest, provided that the original system is stable prior to implementing the retrofit control. Subsequently, we find out a unique internal structure of the retrofit controllers, assuming that the interaction input signal from the neighboring subsystems is measurable. Furthermore, we show that the simultaneous implementation of multiple retrofit controllers, designed by individual subcontroller designers, can improve the upper bound of the overall control performance. Finally, the practical significance of the method is demonstrated via an illustrative example of frequency regulation using the IEEE 68-bus power system model.

  • Gain-preserving data-driven approximation of the koopman operator and its application in robust controller design

    Hara K., Inoue M.

    Mathematics (Mathematics)  9 ( 9 )  2021.05

     View Summary

    In this paper, we address the data-driven modeling of a nonlinear dynamical system while incorporating a priori information. The nonlinear system is described using the Koopman operator, which is a linear operator defined on a lifted infinite-dimensional state-space. Assuming that the L2 gain of the system is known, the data-driven finite-dimensional approximation of the operator while preserving information about the gain, namely L2 gain-preserving data-driven modeling, is formulated. Then, its computationally efficient solution method is presented. An application of the modeling method to feedback controller design is also presented. Aiming for robust stabilization using data-driven control under a poor training dataset, we address the following two modeling problems: (1) Forward modeling: The data-driven modeling is applied to the operating data of a plant system to derive the plant model; (2) Backward modeling: L2 gain-preserving data-driven modeling is applied to the same data to derive an inverse model of the plant system. Then, a feedback controller composed of the plant and inverse models is created based on internal model control, and it robustly stabilizes the plant system. A design demonstration of the data-driven controller is provided using a numerical experiment.

  • Predictive Control of Cyber-Physical Systems

    Maestre J.M., Chanfreut P., Martín J.G., Masero E., Inoue M., Camacho E.F.

    RIAI - Revista Iberoamericana de Automatica e Informatica Industrial (RIAI - Revista Iberoamericana de Automatica e Informatica Industrial)  19 ( 1 ) 1 - 12 2021

    ISSN  16977912

     View Summary

    Predictive control encompasses a family of controllers that continually replan the system inputs during a certain time horizon to optimize their expected evolution according to a given criterion. This methodology has among its current challenges the adaptation to the paradigm of the so-called cyber-physical systems, which are composed of computers, sensors, actuators and physical entities of various kinds, including robots and even human beings who exchange information to control physical processes. This tutorial introduces the core concepts for the application of predictive control to cyber-physical systems by reviewing a series of examples that exploit the versatility of this design framework so as to solve the challenges presented by 21st century applications.

  • Grid resilience enhancement by reference shaping based on gray-box model

    Sadamoto T., Inoue M., Kaneko O.

    IEEJ Transactions on Electronics, Information and Systems (IEEJ Transactions on Electronics, Information and Systems)  141 ( 5 ) 694 - 703 2021

    ISSN  03854221

     View Summary

    We propose a new method for enhancing resiliency of power systems integrated with renewable power resources. In the proposed method, we design a resilience enhancer that shapes reference values, generated by existing any power flow computation scheme such as EDC, so that the influence of faults on power system dynamics can be appropriately mitigated. The design of the resilience enhancer is based on so-called gray-box modeling, which is a fusion of a few prior knowledge on the grid and data acquired in its healthy operation. The effectiveness of the proposed method is investigated through a three-machine system integrated with three solar farms.

  • On the Instant Iterative Learning MPC for Nonlinear Systems

    Sato K., Sawada K., Inoue M.

    2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020 (2020 59th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2020)     1166 - 1171 2020.09

    ISSN  9781728110899

     View Summary

    Model predictive control (MPC) is one of the methods which optimizes the trajectory of the system with the constraints from predicted states of the system. A number of researches have studied its applications, for example, online optimization methods and fast solvers for nonlinear systems, because of its effectiveness. We propose one of the methods to apply online MPC to nonlinear systems based on instant MPC (iMPC). We recast iterative learning MPC (ILMPC) for nonlinear systems as iMPC via the primal-dual gradient algorithm, which we name "i-ILMPC". Finally, a numerical simulation is performed to demonstrate its effectiveness.

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Papers, etc., Registered in KOARA 【 Display / hide

Reviews, Commentaries, etc. 【 Display / hide

  • 最適エネルギー管理のためのサイバーフィジカルシステムの系統的設計

    井上 正樹,畑中 健志

    特集「Society 5.0のためのシステム制御技術」,計測と制御 58 ( 8 ) 612 - 617 2019.08

    Article, review, commentary, editorial, etc. (scientific journal), Joint Work

  • 制御系設計の課題を実感するための導入実験

    浅井 徹,大須賀 公一,石川 将人,杉本 靖博,井上 正樹

    計測と制御 54 ( 3 ) 152 - 158 2015

    Article, review, commentary, editorial, etc. (scientific journal), Joint Work

  • Robust bifurcation analysis toward analysis and synthesis of bio-molecular circuits

    INOUE Masaki

    Systems, Control, and Information 58 ( 8 ) 321 - 326 2014

    Article, review, commentary, editorial, etc. (scientific journal), Single Work

Presentations 【 Display / hide

  • 自動車エンジン燃焼系の信頼度付きモデリング

    森川 浩太朗,井上 正樹,村岡 光夫,下城 孝名子,橋上 栄ニ,足立 修一

    第3回計測自動制御学会制御部門マルチシンポジウム, 

    2016.03

    Oral presentation (general)

  • 伝達関数へのモーメント制約のもとでの部分空間同定法

    井上 正樹,松林 綾香,足立 修一

    第3回計測自動制御学会制御部門マルチシンポジウム, 

    2016.03

    Oral presentation (general)

  • フィードバック接続によって外乱抑制性能が向上するシステムの一クラス:gamma-正実性を用いた解析

    浦田 賢吾,井上 正樹

    第3回計測自動制御学会制御部門マルチシンポジウム, 

    2016.03

    Oral presentation (general)

  • 自然エネルギーの動的モデリングと超短時間先予測

    浦田 賢吾,清岡 研治,井上 正樹

    第58回自動制御連合講演会, 

    2015.11

    Oral presentation (general)

  • 周波数特性の事前情報を利用した部分空間同定法

    阿部 侑真,井上 正樹,足立 修一

    第58回自動制御連合講演会, 

    2015.11

    Oral presentation (general)

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Research Projects of Competitive Funds, etc. 【 Display / hide

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Awards 【 Display / hide

  • 計測自動制御学会 制御部門マルチシンポジウム賞

    2022.03

    Type of Award: Award from Japanese society, conference, symposium, etc.

  • 計測自動制御学会 制御部門大会賞

    井上正樹,吉村翔, 2020.03, 計測自動制御学会制御部門

    Type of Award: Award from Japanese society, conference, symposium, etc.

  • 計測自動制御学会 制御部門パイオニア賞

    井上正樹, 2020.03, 計測自動制御学会制御部門

    Type of Award: Award from Japanese society, conference, symposium, etc.

     View Description

    「人の意思決定を含むシステムに対する制御理論の開拓と展開」に対して

  • Asian Journal of Control Outstanding Reviewer for 2019

    2020, Chinese Automatic Control Society

    Type of Award: Honored in official journal of a scientific society, scientific journal

  • エヌエフ基金 研究開発奨励賞

    井上正樹, 2019.11, エヌエフ基金

    Type of Award: Other

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Courses Taught 【 Display / hide

  • PRESENTATION TECHNIQUE

    2022

  • MATHEMATICS FOR APPLIED PHYSICS (C)

    2022

  • LABORATORY IN SCIENCE

    2022

  • INDEPENDENT STUDY ON FUNDAMENTAL SCIENCE AND TECHNOLOGY

    2022

  • GRADUATE RESEARCH ON FUNDAMENTAL SCIENCE AND TECHNOLOGY 2

    2022

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Courses Previously Taught 【 Display / hide

  • 動的システムのモデリングと制御,演習

    大阪大学工学部

    2018.04
    -
    2019.03

  • 自然科学実験(物理学)

    Keio University

    2015.04
    -
    2016.03

    Spring Semester, Laboratory work/practical work/exercise

  • 物理情報工学実験AB

    Keio University

    2015.04
    -
    2016.03

    Spring Semester, Laboratory work/practical work/exercise

  • 物理情報工学実験CD

    Keio University

    2014.04
    -
    2015.03

    Autumn Semester, Laboratory work/practical work/exercise

  • 物理情報工学実験AB

    Keio University

    2014.04
    -
    2015.03

    Spring Semester, Laboratory work/practical work/exercise

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Educational Activities and Special Notes 【 Display / hide

  • 大阪大学工学研究科 研究科長賞

    2011.04
    -
    2012.03

    , Device of Educational Contents

 

Memberships in Academic Societies 【 Display / hide

  • 電気学会, 

    2017
    -
    2020.03
  • IEEE, Control Systems Society, 

    2012
    -
    Present
  • 計測自動制御学会, 

    2008
    -
    Present
  • システム制御情報学会, 

    2008
    -
    Present

Committee Experiences 【 Display / hide

  • 2022.01
    -
    Present

    副主査・幹事, 計測自動制御学会制御部門 人とつながる制御システム調査研究会

  • 2020.10
    -
    Present

    TC Member, IFAC Technical Committee on Social Impact of Automation, TC 9.2

  • 2019.04
    -
    2020.12

    幹事, 計測自動制御学会制御部門 Cyber-Physical & Human システム調査研究会

  • 2019.03
    -
    Present

    アソシエイトエディタ, 計測自動制御学会論文集

  • 2019

    Program Committee, SICE International Symposium on Control Systems 2019

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