Print

2024　System Modeling

Font size  SML

Register update notification mail Add to favorite lecture list
Academic unit or major
Undergraduate major in Systems and Control Engineering
Instructor(s)
Ishizaki Takayuki 
Class Format
Lecture     
Media-enhanced courses
Day/Period(Room No.)
-
Group
-
Course number
SCE.C302
Credits
2
Academic year
2024
Offered quarter
3Q
Syllabus updated
2024/3/14
Lecture notes updated
-
Language used
Japanese
Access Index

Course description and aims

System modeling in systems and control engineering has its origins in Watt's mechanical systems related to steam engines and speed regulators. Since then, through the industrial revolution and the systems revolution, artifacts in the real world have continued to become more complex. In this course, students will learn about modeling of systems involved in extracting features of complex real systems and their diversity through specific examples. Students will also learn the basics of systems theory, low-dimensional approximation theory, partial differential equation systems, mathematical optimization, and system identification, which are necessary for system modeling. Finally, as an advanced topic, the significance and usefulness of modeling in academic research will be discussed.

Student learning outcomes

By taking this course, students will understand that "better models" in the natural sciences can vary depending on the purpose, and representation. The course also aims to enable students to model dynamical systems appropriately based on concepts such as controllability, observability, and stability in systems and control engineering, as well as mathematical optimization. In the exercises, students will acquire basic numerical methods by solving optimization problems and identifying systems using MATLAB.

Keywords

Mathematical modeling, Controllable and observable canonical decomposition, Discrete-time models, Markov parameter matching, Balanced realization, Distributed parameter systems, Lagrange multiplier method, Convex conjugacy, FIR model identification

Competencies that will be developed

✔ Specialist skills Intercultural skills Communication skills Critical thinking skills Practical and/or problem-solving skills

Class flow

1) A slide lecture lasting approximately 80 minutes.
2) Students will have approximately 20 minutes to complete exercises to review the lecture content.

Course schedule/Required learning

  Course schedule Required learning
Class 1 System Modeling Confirm the position of the subject on the portfolio. To be able to give an overview of system modeling.
Class 2 Various Models Understand a wide range of models that exist in the world.
Class 3 Fundamentals of Systems Theory 1 Understand controllable and observable canonical decompositions.
Class 4 Fundamentals of Systems Theory 2 Acquire the basics of system analysis based on energy functions.
Class 5 Fundamentals of Systems Theory 3 To acquire the basics of discrete-time systems.
Class 6 Low-dimensional modeling 1 To acquire the basics of model reduction based on projection.
Class 7 Low-dimensional modeling 2 Understand model reduction based on principal component analysis.
Class 8 Distributional Parameter Systems 1 To acquire the basics of partial differential equation systems.
Class 9 Distributional Parameter Systems 2 Understand transfer functions and spatial discretization of partial differential equations.
Class 10 Mathematical Optimization 1 To be able to give an overview of mathematical optimization. Understand unconstrained quadratic programming problems.
Class 11 Mathematical Optimization 2 Understand the concept of convex optimization problems with equality and inequality constraints.
Class 12 Mathematical Optimization 3 Understand Lagrangian relaxation and convex conjugacy of equality and inequality constraints.
Class 13 Fundamentals of System Identification Learn how to identify FIR and ARX models.
Class 14 Advanced Topics Understand advanced topics.

Out-of-Class Study Time (Preparation and Review)

To enhance effective learning, students are encouraged to spend approximately 100 minutes preparing for class and another 100 minutes reviewing class content afterwards (including assignments) for each class.
They should do so by referring to textbooks and other course material.

Textbook(s)

Materials will be distributed as needed.

Reference books, course materials, etc.

https://sites.google.com/sc.e.titech.ac.jp/sce-c302sysmod

Assessment criteria and methods

1. Submission of reports on exercises to be worked on in each lecture (50%)
2. Submission of reports assigned in the final lecture (50%)

Related courses

  • SCE.C201 ： Fundamentals of Dynamical Systems
  • SCE.C202 ： Feedback Control
  • SCE.C301 ： Linear System Theory
  • SCE.E202 ： Information Processing and Programming (Systems and Control)
  • SCE.C401 ： System Identification and Estimation
  • SCE.C402 ： Robust Control

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students must have successfully completed SCE.C.201, SCE.C.202, SCE.C.301, and SCE.E.202 or have equivalent knowledge.

Contact information (e-mail and phone)    Notice : Please replace from "[at]" to "@"(half-width character).

email: ishizaki[at]sc.e.titech.ac.jp

Page Top