WSS20501 Advanced Data-Driven Systems Design

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Smart Innovation Program

Lecture Code WSS20501 Subject Classification Specialized Education

Subject Name データ駆動型システム特論

Subject Name
（Katakana）データクドウガタシステムトクロン

Subject Name in
English Advanced Data-Driven Systems Design

Instructor KINOSHITA TAKUYA

Instructor
(Katakana) キノシタ　タクヤ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Mon5-8：ENG 101

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (simultaneous interactive)

Credits 2.0 Class Hours/Week 4 Language of Instruction B : Japanese／English

Course Level 6 : Graduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 11 : Electrical, Systems, and Control Engineering

Eligible Students Students in the Graduate School of Advanced Science and Engineering

Keywords data-driven control, control system design, PID control

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline This lecture will cover the fundamentals of data-driven control systems. The main contents of this lecture are as follows.
(1) Basic concept of data-driven control methods for linear systems
(2) Basic concept of data-driven control methods for nonlinear systems
(3) Basic concept of machine learning

Class Schedule lesson1: Basic Concepts of Data-Driven Control
(Model-based control, PID control, data-driven control)
lesson2: Fundamentals of Numerical Simulation
(Matlab/Simulink exercise)
lesson3: Data-driven control: FRIT method
(reference model, pseudo-reference signal)
lesson4: Data-driven control: FRIT method
(PC exercise)
lesson5: Nonlinear Optimization Method
（Gauss-Newton method)
lesson6: Nonlinear Optimization Method
(PC exercise)
lesson7: Data-driven control: VRFT method
(Comparison with FRIT method)
lesson8: Data-driven control: VRFT method
(PC exercise)
lesson9: Machine Learning: Support Vector Machine
(classification problems)
lesson10: Machine Learning: Support Vector Machine
(PC exercise)
lesson11: Data-driven Control: Database-driven Control
(Overview of database-driven control)
lesson12: Data-driven Control: Database-driven Control
(Neighborhood data selection method, online learning)
lesson13: Data-driven Control: Database-driven Control
(PC Exercise 1)
lesson14: Data-driven Control: Database-driven Control
(PC Exercise 2)
lesson15: Introduction to industrial application case studies (final report)

Text/Reference
Books,etc. Power Point materials are used in the lecture.

PC or AV used in
Class,etc.

(More Details) Power Point

Learning techniques to be incorporated

Suggestions on
Preparation and
Review PC exercises will be conducted to deepen understanding of the lecture content. Therefore, it is effective to keep this in mind when preparing for and reviewing the lectures.

Requirements It is advisable to take lectures on the basics of differentiation and integration, linear algebra, and control engineering.
Since MATLAB / Simulink (Version is 2020a) is used in the exercise, consult with the instructor in advance if you cannot install it on your PC.

Grading Method Exercises (including computer exercises) 30%, final report 60%, Attitude towards participation 10%

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Computer exercises will be given as part of the lecture. Bring your computer on the designated day.

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Smart Innovation Program
Lecture Code	WSS20501	Subject Classification	Specialized Education
Subject Name	データ駆動型システム特論
Subject Name （Katakana）	データクドウガタシステムトクロン
Subject Name in English	Advanced Data-Driven Systems Design
Instructor	KINOSHITA TAKUYA
Instructor (Katakana)	キノシタ　タクヤ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Mon5-8：ENG 101
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (simultaneous interactive)

Credits	2.0	Class Hours/Week	4	Language of Instruction	B : Japanese／English
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	11 : Electrical, Systems, and Control Engineering
Eligible Students	Students in the Graduate School of Advanced Science and Engineering
Keywords	data-driven control, control system design, PID control
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	This lecture will cover the fundamentals of data-driven control systems. The main contents of this lecture are as follows. (1) Basic concept of data-driven control methods for linear systems (2) Basic concept of data-driven control methods for nonlinear systems (3) Basic concept of machine learning
Class Schedule	lesson1: Basic Concepts of Data-Driven Control (Model-based control, PID control, data-driven control) lesson2: Fundamentals of Numerical Simulation (Matlab/Simulink exercise) lesson3: Data-driven control: FRIT method (reference model, pseudo-reference signal) lesson4: Data-driven control: FRIT method (PC exercise) lesson5: Nonlinear Optimization Method （Gauss-Newton method) lesson6: Nonlinear Optimization Method (PC exercise) lesson7: Data-driven control: VRFT method (Comparison with FRIT method) lesson8: Data-driven control: VRFT method (PC exercise) lesson9: Machine Learning: Support Vector Machine (classification problems) lesson10: Machine Learning: Support Vector Machine (PC exercise) lesson11: Data-driven Control: Database-driven Control (Overview of database-driven control) lesson12: Data-driven Control: Database-driven Control (Neighborhood data selection method, online learning) lesson13: Data-driven Control: Database-driven Control (PC Exercise 1) lesson14: Data-driven Control: Database-driven Control (PC Exercise 2) lesson15: Introduction to industrial application case studies (final report)
Text/Reference Books,etc.	Power Point materials are used in the lecture.
PC or AV used in Class,etc.
(More Details)	Power Point
Learning techniques to be incorporated
Suggestions on Preparation and Review	PC exercises will be conducted to deepen understanding of the lecture content. Therefore, it is effective to keep this in mind when preparing for and reviewing the lectures.
Requirements	It is advisable to take lectures on the basics of differentiation and integration, linear algebra, and control engineering. Since MATLAB / Simulink (Version is 2020a) is used in the exercise, consult with the instructor in advance if you cannot install it on your PC.
Grading Method	Exercises (including computer exercises) 30%, final report 60%, Attitude towards participation 10%
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Computer exercises will be given as part of the lecture. Bring your computer on the designated day.
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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