2022 System Identification and Estimation

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Academic unit or major
Graduate major in Systems and Control Engineering
Yamakita Masaki 
Class Format
Lecture    (Face-to-face)
Media-enhanced courses
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Academic year
Offered quarter
Syllabus updated
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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.


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.

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.


Document is distributed in each lesson.

Reference books, course materials, etc.

Dan Simon: Optimal State Estimation (John Wiley & Sons)

Assessment criteria and methods


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.

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