▶Japanese
Post to SNS
- Home
- > School of Engineering
- > Graduate major in Systems and Control Engineering
- > System Identification and Estimation
- School of Science
-
School of Engineering
- Undergraduate major in Mechanical Engineering
- Undergraduate major in Systems and Control Engineering
- Undergraduate major in Electrical and Electronic Engineering
- Undergraduate major in Information and Communications Engineering
- Undergraduate major in Industrial Engineering and Economics
- Common courses
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
- School of Environment and Society
- 工学院，物質理工学院，環境・社会理工学院共通科目
- Liberal arts and basic science courses
- First-Year Courses
- School of Science
- School of Engineering
- School of Materials and Chemical Technology
- School of Computing
- School of Life Science and Technology
-
School of Environment and Society
- Department of Architecture and Building Engineering
- Department of Civil and Environmental Engineering
- Department of Transdisciplinary Science and Engineering
- Department of Social and Human Sciences
- Department of Innovation Science
- Graduate major in Technology and Innovation Management
- Common courses
- Liberal arts and basic science courses
- Academic unit or major
- Graduate major in Systems and Control Engineering
- Instructor(s)
- Yamakita Masaki
- Class Format
- Lecture
- Media-enhanced courses
- Day/Period(Room No.)
- Mon3-4(S514)
- Group
- -
- Course number
- SCE.C401
- Credits
- 1
- Academic year
- 2017
- Offered quarter
- 3Q
- Syllabus updated
- 2017/3/17
- Lecture notes updated
- -
- Language used
- English
- Access Index
-
Course description and aims
First mathematical knowledge about plant modeling which is needed for design of control systems is summarized, then basic and practical system identification procedures are explained. Also, nonlinear filtering techniques needed for prediction of behavior of systems in future are studied.
Student learning outcomes
For state estimation and system identification, general knowledge about signal processing and stochastic process are needed. In this course, such basic knowledge is studied totally, and concrete state estimation and system identification algorithms are studied.
Keywords
system modeling, system identification, state estimation, nonlinear filtering
Competencies that will be developed
| ✔ Specialist skills | Intercultural skills | Communication skills | Critical thinking skills | ✔ Practical and/or problem-solving skills |
Class flow
Each class contains one topic basically.
Course schedule/Required learning
| Course schedule | Required learning | |
|---|---|---|
| Class 1 | Classes of models and analysis tools | Classes of models and tools for analysis are studied. |
| Class 2 | Stochastic process and stochastic differential equation | Concept of stochastic process is mastered and meaning of stochastic differential equation is studied. |
| Class 3 | Basic system identification procedure: nonparametric algorithms | As a basic system identification procedure, nonparametric algorithm is studied. |
| Class 4 | Basic system identification procedure: parametric algorithms | As a basic system identification procedure, parametric algorithm is studied. |
| Class 5 | Advanced system identification procedure: subspace system identification algorithm | As an advanced system identification procedure, subspace system identification algorithm is studied. |
| Class 6 | Minimum variance estimation | Concept of minimum variance estimation is studied. |
| Class 7 | Nonlinear filtering for discrete time systems | Nonlinear filtering algorithm for discrete time systems is studied. |
| Class 8 | Discrete-continuous Hybrid nonlinear filtering | Continuous-Discrete hybrid nonlinear filtering algorithm is studied. |
Textbook(s)
Document is distributed in each lesson.
Reference books, course materials, etc.
Dan Simon: Optimal State Estimation (John Wiley & Sons)
Assessment criteria and methods
Report
Related courses
- TSE.M203 ： Theory of Linear System
- SCE.C532 ： Nonlinear Control: Geometric Approach
- SCE.C531 ： Nonlinear and Adaptive Control
- SCE.C302 ： System Modeling
Prerequisites (i.e., required knowledge, skills, courses, etc.)
Not required.
