2024 Faculty Courses School of Engineering Department of Systems and Control Engineering Graduate major in Systems and Control Engineering
System Identification and Estimation
- Academic unit or major
- Graduate major in Systems and Control Engineering
- Instructor(s)
- Masaki Yamakita
- Class Format
- Lecture (Face-to-face)
- Media-enhanced courses
- -
- Day of week/Period
(Classrooms) - 3-4 Mon
- Class
- -
- Course Code
- SCE.C401
- Number of credits
- 100
- Course offered
- 2024
- Offered quarter
- 3Q
- Syllabus updated
- Mar 14, 2025
- Language
- English
Syllabus
Course overview and goals
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.
Course description and aims
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
- 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/Objectives
| Course schedule | Objectives | |
|---|---|---|
| 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. |
Study advice (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)
Document is distributed in each lesson.
Reference books, course materials, etc.
Dan Simon: Optimal State Estimation (John Wiley & Sons)
Evaluation methods and criteria
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
Not required.