Inoue, Masaki

写真a

Affiliation

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

Position

Associate Professor

E-mail Address

E-mail address

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

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

  • Dynamics/Control

  • Control engineering/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

  • Air Traffic Management, 

    2018
    -
    Present

  • 大規模複雑システムの制御, 

    2017
    -
    Present

  • Smart Grid, 

    2015
    -
    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

 

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

  • 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.

  • 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.

  • Performance improvement via iterative connection of passive systems

    URATA Kengo, INOUE Masaki, ISHIZAKI Takayuki, IMURA Jun-ichi

    IEEE Transactions on Automatic Control (IEEE Transactions on Automatic Control)  65 ( 3 ) 1325 - 1332 2020

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  00189286

     View Summary

    © 1963-2012 IEEE. This paper addresses model-set-based quantitative analysis of feedback systems. In particular, we find a model set describing the subsystems such that the performance improvement of the feedback system is achieved. To this end, we introduce the parameter-integrated passivity to accurately describe each passive subsystem and their feedback system. A model set describing passive systems is characterized by the two matrix parameters. The matrix parameters enable to evaluate the L_2-gain 'of the model set', which is defined as the L_2-gain of the worst-case system in the model set. With the parameter-integrated passivity, the quantitative analysis of a feedback system composed of two passive subsystems is provided as the parameter transition. Then, we find conditions on the matrix parameters to achieve the performance improvement such that the L_2-gain of the model set describing the feedback system is strictly less than that describing the subsystems. Subsequently, the performance improvement of the feedback system is extended to that of an iterative feedback system, which is a network system constructed by the feedback connection of multiple subsystems in a step-by-step manner. Then, we find conditions on the passivity parameters describing the baseline subsystem to achieve a gradual performance improvement with the subsystem connection.

  • Instant distributed model predictive control for constrained linear systems

    Martin FIGURA, Lanlan SU, Vijay GUPTA, INOUE Masaki

    Proceedings of the American Control Conference 2020 (Proceedings of the American Control Conference)  2020-July   4582 - 4587 2020

    Research paper (international conference proceedings), Joint Work, Accepted,  ISSN  9781538682661

     View Summary

    © 2020 AACC. Distributed optimal control has emerged as an exciting possibility; however, existing algorithms tend to require excessive computational time and thus may not be able to stabilize systems with fast dynamics. We develop instant distributed model predictive control (iDMPC) with a realization of the primal-dual algorithm embedded in the controller dynamics. Under assumptions on fast communication, we show that the input and state trajectories of iDMPC are equivalent to a centralized suboptimal MPC scheme. We utilize a dissipativity analysis to show that the closed-loop system trajectories asymptotically converge to a desired reference.

  • Learning Koopman operator under dissipativity constraints

    HARA Keita, INOUE Masaki, SEBE Noboru

    Preprints of the 21st IFAC World Congress (IFAC-PapersOnLine)  53 ( 2 ) 1169 - 1174 2020

    Research paper (international conference proceedings), Joint Work, Accepted

     View Summary

    This paper addresses a learning problem for nonlinear dynamical systems with incorporating any specified dissipativity property. The nonlinear systems are described by the Koopman operator, which is a linear operator defined on the infinite-dimensional lifted state space. The problem of learning the Koopman operator under specified quadratic dissipativity constraints is formulated and addressed. The learning problem is in a class of the non-convex optimization problem due to nonlinear constraints and is numerically intractable. By applying the change of variable technique and the convex overbounding approximation, the problem is reduced to sequential convex optimization and is solved in a numerically efficient manner. Finally, a numerical simulation is given, where high modeling accuracy achieved by the proposed approach including the specified dissipativity is demonstrated.

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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

    Introduction and explanation (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

    Introduction and explanation (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

    Introduction and explanation (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: Awards of National Conference, Council and Symposium

  • 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: Awards of National Conference, Council and Symposium

     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: Celebration by Official journal of a scientific society or Academic 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 Awards

  • 計測自動制御学会 制御部門大会技術賞

    松井健, 井上正樹, 足立修一, 上野将樹, 豊島弘和, 堤優二郎, 2019.03, 計測自動制御学会制御部門

    Type of Award: Awards of National Conference, Council and Symposium

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Courses Taught 【 Display / hide

  • PRESENTATION TECHNIQUE

    2021

  • MATHEMATICS FOR APPLIED PHYSICS (C)

    2021

  • LABORATORY IN SCIENCE

    2021

  • INDEPENDENT STUDY ON FUNDAMENTAL SCIENCE AND TECHNOLOGY

    2021

  • GRADUATE RESEARCH ON FUNDAMENTAL SCIENCE AND TECHNOLOGY 2

    2021

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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

  • 物理情報工学実験AB

    Keio University, 2015, Spring Semester, Major subject, Laboratory work/practical work/exercise

  • 自然科学実験(物理学)

    Keio University, 2015, Spring Semester, General education subject, Laboratory work/practical work/exercise

  • 物理情報工学実験CD

    Keio University, 2014, Autumn Semester, Major subject, Laboratory work/practical work/exercise

  • 物理情報工学実験AB

    Keio University, 2014, Spring Semester, Major subject, 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

  • 2019.04
    -
    2020.12

    幹事, 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

  • 2018.01
    -
    2019.12

    委員, 計測自動制御学会 制御部門 超スマート社会実現のためのシステム制御技術調査研究会

  • 2018

    Program Committee, SICE International Symposium on Control Systems 2018

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