Ohmori, Hiromitsu

写真a

Affiliation

Faculty of Science and Technology, Department of System Design Engineering School of Integrated Design Engineering (Yagami)

Position

Professor

Related Websites

External Links
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Profile 【 Display / hide 】

  • Hiromitsu Ohmori received Bachelor of Electrical Engineering, Master of Electrical Engineering and Ph.D from Keio University, Japan in 1983, 1985 and 1988, respectively. From April 1988 he was the instructor of Department of Electrical Engineering, Keio University, Japan. From April 1991 he was the Assistant Professor of the same department. From April 1996 he was the Associate Professor of Department of System Design Engineering, Keio University. Currently he is working as Professor at the same department. His research interests are in the field of adaptive control, robust control, nonlinear control and their applications. He is member of IEEE, SICE, ISCIE, IEE, IEICE, and EICA etc.

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Career 【 Display / hide 】

  • 1988.04
    -
    1991.03

    慶應義塾大学理工学部電気工学科 ,助手

  • 1991.04
    -
    1996.03

    慶應義塾大学理工学部電気工学科 ,専任講師

  • 1996.04
    -
    2003.03

    慶應義塾大学理工学部システムデザイン工学科、助教授

  • 1997.04
    -
    1998.03

    兼慶應義塾大学学生総合センター就職部門（矢上支部） ,委員

  • 2003.04
    -
    Present

    慶應義塾大学理工学部システムデザイン工学科 ,教授

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Academic Background 【 Display / hide 】

  • 1983.03

    Keio University, Faculty of Engineering, (電気工学科)

    University, Graduated

  • 1985.03

    Keio University, Graduate School, Division of Engineering, 電気工学専攻

    Graduate School, Completed, Master's course

  • 1988.03

    Keio University, Graduate School, Division of Science and Engineering, 電気工学専攻

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • 工学博士, Keio University, Coursework, 1988.03

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Research Areas 【 Display / hide 】

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

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Research Keywords 【 Display / hide 】

  • 制御工学／非線形工学／適応学習制御システム／ロバスト制御システム／システムモデリングとデザイン

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Books 【 Display / hide 】

  • 基礎からわかる　自動車エンジンのモデルベースト制御

    金子成彦 監修、山崎由大 編著、大森 浩充、平田光男、水本郁朗、一柳満久、松永彰生、神田智博, コロナ社, 2019.02

    Scope: 4章第１節、4章第2節、コラム4.1　24.9頁

  • データに基づく性能指向型制御システム設計

    OHMORI HIROMITSU, Sugisaki, 電気学会, 2017.06

    Scope: 第11章

  • Adaptive PID Control for Perfect Tracking Problem of MIMO Systems

    Kenichi Tamura, OHMORI HIROMITSU, INTECH, OPEN ACCSC Publisher, 2011

  • 自動車エンジンのモデリングと制御

    OHMORI HIROMITSU, コロナ社, 2011

    Scope: 第6章吸気バルブリフト量に着目したエンジン制御（pp.120-150）

  • Linear Control Theory

    Kiyotaka Shimizu, OHMORI HIROMITSU, Bifukan, 2003.05

    Scope: Robust Control

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Papers 【 Display / hide 】

  • State of Charge Estimation for Lithium-Ion Battery Based on Fractional-Order Kreisselmeier-Type Adaptive Observer

    Murakami T., Ohmori H.

    Machines 12 ( 10 )  2024.10

    　View Summary

    For the safe and efficient use of lithium-ion batteries, the state of charge (SOC) is a particularly important state variable. In this paper, we propose a method for the online estimation of SOC and model parameters based on a fractional-order equivalent circuit model. Firstly, we constructed a fractional-order battery model that includes pseudo-capacitance and determined the values of the circuit elements offline using the least squares method from actual input–output data based on the driving profile of an automobile. Compared to the integer-order battery model, we confirmed that the proposed fractional-order battery model has higher accuracy. Secondly, we constructed a fractional-order Kreisselmeier-type adaptive observer as an observer that performs state estimation and parameter adjustment simultaneously. Applying the general adaptive law to the battery model results in a redundant design with many adjustable parameters, so we proposed an adaptive law that reduces the number of adjustable parameters without compromising the stability of the observer. The effectiveness of the proposed method was verified through numerical simulations. As a result, the high estimation accuracy and convergence of the proposed adaptive law were confirmed.

  • Feedback Error Learning Control for Airpath System by Using Oxygen Concentration Adaptive Observer

    Hashimoto Y., Ohmori H.

    Comodia 2022 10th International Conference on Modeling and Diagnostics for Advanced Engine Systems    399 - 407 2022.07

    　View Summary

    To deal with stringent exhaust emission regulations, an exhaust gas recirculation (EGR) system is widely used in internal combustion engines. However, appropriate control of EGR is difficult because of the gas transport delay and unavailability of the EGR rate. In this paper, a model-based control approach for engine airpath systems is proposed. A physical model of engine airpath considering the transport delay in EGR is constructed. An adaptive observer is introduced to estimate the EGR rate by using available quantities. Feedback error learning control is applied to the airpath system to deal with a characteristic change of the controlled plant. We validate the proposed method by numerical simulations.

  • Energy Optimization of Hybrid electric Vehicles Using Deep Q-Network

    Yokoyama T., Ohmori H.

    2022 61st Annual Conference of the Society of Instrument and Control Engineers of Japan SICE 2022    827 - 832 2022

    　View Summary

    Hybrid electric vehicles are positioned as an intermediate form between gasoline and electric vehicles, contributing to lower fuel consumption and emissions. Map control is used for conventional engine control. This method maps optimal values from experimental data, and it has been pointed out that the capacity of the map is increasing and that it is difficult to respond to increasingly sophisticated control objectives. In this paper, we first present a model of a series-parallel hybrid electric vehicle and propose a method using Deep Q-Network, a typical reinforcement learning technique. Through numerical simulation, we verify that the SOC is within an acceptable range throughout the entire run and that energy efficiency can be improved compared to existing map control.

  • Real-Time Electricity Retail Pricing Dual Optimization With Context-Based Fuzzy Optimal Algorithm

    Jamaludin J., Ohmori H., Azzuhri S.R.

    Electric Power Components and Systems 50 ( 6-7 ) 374 - 385 2022

    ISSN  15325008

    　View Summary

    Despite its bright prospect to promote affordable energy, one of the main concerns of real-time retail pricing for electricity is price volatility that would create potential bill shocks especially for low-income consumers. This would demotivate consumers to participate in real-time pricing scheme or to act as responsive participants to reduce peak demand. As a motivation to address this concern, this article proposes dual optimization method with fuzzy optimal algorithm to solve the optimization problem presented in the real-time pricing model with the aims to reduce price volatility and to lower the minimum price further. The fuzzy optimal algorithm applies a context-based fuzzy inferencing and a gradient adjustment to arrive at the optimal retail price for every time interval. Context-based fuzzy inferencing allows fuzzy value redefinition so that the desired level of precision is preserved. Gradient adjustment simplifies the derivation of the optimal retail price via a series of iterations. Simulation results reveal that price fluctuations are able to be reduced which would create stability and avoid undesired price spikes. At the same time, the results confirm that further reduction in the minimum retail price can be achieved which would improve welfare benefits while maintaining the desired level of consumer satisfaction.

  • Feedback error learning control using OS-ELM for SI engine airpath systems

    Wang D., Hashimoto Y., Ohmori H.

    IFAC Papersonline 54 ( 10 ) 182 - 188 2021

    　View Summary

    Currently, due to the stricter emission legislation, spark-ignition (SI) engine with low pressure exhaust gas recirculation (LP-EGR) has become a research hotspot. However, with the increasing complexity of engine, it is difficult to obtain sufficient control performance by conventional MAP control or PID control. This paper focuses on the airpath system of a SI engine with low pressure EGR, proposes a new model-based control method for the model of the airpath system based on the Feedback Error Learning control using online sequential-extreme learning machine (OS-ELM) as controller algorithm. In this paper, the airpath model has been built and effectiveness of the proposed control method has been confirmed on the model.

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

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Reviews, Commentaries, etc. 【 Display / hide 】

  • データに基づく性能指向型制御システム設計

    OHMORI HIROMITSU, Sugisaki

    データに基づく複雑性の計量とその応用 (電気学会　電子・情報・システム部門，制御技術委員会)  1411   42 - 52 2017.11

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

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Presentations 【 Display / hide 】

  • 非線形系に対するモデル規範型適応制御系へのカーネルトリックの応用

    Cuoghi Ludovico、大森浩充（慶應義塾大学）

    電気学会研究会　C部門制御研究会、テーマ（制御理論・制御技術一般（スマートシステムと制御技術シンポジウム2020） (賀茂泉館４Ｆ「泉ホール」) , 

    2020.01
    , 

    Oral presentation (general), 電気学会

  • フィードバック誤差学習を用いたディーゼルエンジン吸排気系の制御

    酒井 大地，大森 浩充（慶應義塾大学）

    第62回自動制御連合講演会 (札幌コンベンションセンター) , 

    2019.11
    , 

    Oral presentation (general), 日本機械学会（幹事学会）

  • OS-ELMを用いたフィードバック誤差学習によるディーゼルエンジン燃焼制御

    楠瀬 弘城，大森 浩充（慶應義塾大学）

    第62回自動制御連合講演会 (札幌コンベンションセンター) , 

    2019.11
    , 

    Oral presentation (general), 日本機械学会（幹事学会）

  • ニュートン型極値制御を用いた電気飛行機における回生電力最大化制御

    福田 直輝，大森 浩充（慶應義塾大学）

    第62回自動制御連合講演会 (札幌コンベンションセンター) , 

    2019.11
    , 

    Oral presentation (general), 日本機械学会（幹事学会）

  • 出力制約付き極値制御設計法の提案

    加藤 啓太郎，日高 浩一（東京電機大学），大森 浩充（慶應義塾大学）

    第62回自動制御連合講演会 (札幌コンベンションセンター) , 

    2019.11
    , 

    Oral presentation (general), 日本機械学会（幹事学会）

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Works 【 Display / hide 】

  • 大学共同利用機関法人　情報・システム研究機構　統計数理研究所　平成１８年度　公開講座　上級レベル　適応学習制御理論の新潮流

    OHMORI HIROMITSU

    2006.09
    , 

    Other, Single

    　View Details

    適応極値制御についての，理論研究の歴史と応用事例について述べ，極値探索の基本的な仕組みと，ダイナミックス系，離散時間系，多変数系への拡張を示し，ＰＩＤオートチューニング，タンク系，アンチロックブレーキなどへの応用事例について具体的に示した．

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Awards 【 Display / hide 】

  • 電気学会 論文発表賞

    大森 浩充, 1994.03, 電気学会

  • 第6回安藤博記念 学術奨励賞

    大森 浩充, 1993.06

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Courses Taught 【 Display / hide 】

  • PROBABILITY AND STATISTICS

    2025

  • NONLINEAR ENGINEERING

    2025

  • LABORATORIES IN SCIENCE AND TECHNOLOGY

    2025

  • INDEPENDENT STUDY ON INTEGRATED DESIGN ENGINEERING

    2025

  • GRADUATE RESEARCH ON INTEGRATED DESIGN ENGINEERING 2

    2025

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Social Activities 【 Display / hide 】

  • コロナ社　機械系コアテキストシリーズ　編集員、情報と計測・制御分野担当

    コロナ社

    2014.12
    -
    Present

  • 東京電機大学　非常勤講師　制御工学Ⅱ

    東京電機大学

    2013.10
    -
    2014.03

  • 東京電機大学　制御工学Ⅰ

    2013.04
    -
    2013.09

  • 東京電機大学　制御工学Ⅱ

    2012.10
    -
    2013.03

  • 計測自動制御学会評議員

    計測自動制御学会

    2010.01
    -
    2011.12

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Academic Activities 【 Display / hide 】

  • 日本技術者教育認定機構（JABEE)認定審査

    社団法人　電気学会, 

    2014.05
    -
    2015.03

  • 日本技術者教育認定機構（JABEE)　認定審査

    社団法人　電気学会, 

    2012.05
    -
    2013.03

  • 日本技術者教育認定機構（JABEE)　認定審査

    社団法人　電気学会, 

    2008.05
    -
    2009.03

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Memberships in Academic Societies 【 Display / hide 】

  • 計測自動制御学会　制御部門 データ駆動型社会を支える適応学習制御調査研究会（愛媛大学　大西義浩　教授）　委員, 

    2020.01
    -
    2021.12

  • 電気学会　IoTプラットフォーム上の制御技術に関する調査専門委員会　委員（DIIC1077）, 

    2017.12
    -
    2019.11

  • 計測自動制御学会　制御部門　 データ科学とリンクした次世代の適応学習制御調査研究会（統計数理研究所　宮里義彦教授：委員長）　委員, 

    2017.01
    -
    2019.12

  • 電気学会　先端制御システムの産業応用に関する協同研究委員会 委員（DIIC8085）, 

    2015.11
    -
    2017.10

  • 電気学会　データに基づく性能指向型制御システム調査専門委員会　委員（CCT 1015）, 

    2014.10
    -
    2016.09

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Committee Experiences 【 Display / hide 】

  • 2019.04
    -
    2020.03

    2019年度　共同研究研究員, 大学共同利用機関法人　情報・システム研究機構（統計数理研究所）

  • 2018.04
    -
    2019.03

    2018年度　課題番号（H30-2-2070）、研究課題名（データ科学とリンクした次世代の適応学習制御）、研究代表者（宮里 義彦）　共同研究研究員, 大学共同利用機関法人　情報・システム研究機構（統計数理研究所）

  • 2017.12
    -
    2019.11

    IoTプラットフォーム上の制御技術に関する調査専門委員会（DIIC1077）, 電気学会

  • 2017.04
    -
    2018.03

    Committee Member, 独立行政法人大学評価・学位授与機構学位審査会臨時専門委員　工学・芸術工学専門委員会

  • 2017.04
    -
    2018.03

    2017年度　課題番号（H29-2-2060）、研究課題名（統計数理的アプローチによるユビキタスコンピューティング環境における適応学習制御）、研究代表者（宮里 義彦）共同研究研究員, 大学共同利用機関法人　情報・システム研究機構（統計数理研究所）

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