Hiroshima University Syllabus

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Japanese
Academic Year 2022Year 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,  1Term
Days, Periods, and Classrooms (1T) Mon1-4
Lesson Style Lecture Lesson Style
(More Details)
 
 
Credits 2.0 Class Hours/Week   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
Keywords data-driven control, control system design, PID control 
Special Subject for Teacher Education   Special Subject  
Class Status
within Educational
Program
 
Criterion referenced
Evaluation
 
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%, attendance 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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