System Modeling and Identification

Course ID:  M7201718

Credits: 3

Objective: Let students understand the principle of system parameter identification and related algorithms, so that they can be applied to real systems in the future.

Course Prerequisites: None

Outline:

(1) Teaching handouts; 

(2) Algorithm programming examples; 

(3) Guided homework based on problem-oriented learning.

Teaching Method: 

(1) Classroom lectures will primarily use handouts, supplemented by computer example programs. Students will then write their own programs, with the teacher providing guidance and corrections.

(2) Assignments will focus on Problem-Based Learning (PBL). For each course topic, the teacher has created 'Guided Assignment Questions' that not only state the problems to be solved but also systematically provide the principles or tools needed for solutions, guiding students to present results through data, charts, etc. Students will also have space to express their personal thoughts and insights.

(3) Both midterm and final exams will be practical tests, with question formats similar to the guided assignments.

Reference:  

[1] R. Johansson, System Modeling and Identification, Prentice-Hall, 1993. 

[2] L. Ljung, System Identification, Prentice-Hall, 1987.

Course Schedule (subject to change): 

1.Estimating Transfer Functions from Gain Plots

2.Fitting Experimental Data with Polynomial Functions

3.Estimating Linear Time-Invariant Systems Using Off-line Least Squares

4.Estimating Linear Time-Invariant Systems Using On-line Least Squares

5.Estimating Time-Varying Systems Using Real-time Least Squares

6.Estimating Linear Time-Invariant Systems Using Generalized Least Squares

7.Estimating Time-Varying Systems Using Real-time Generalized Least Squares

8.Estimating Linear Time-Invariant Systems Using Instrumental Variable Method

9.Estimating Time-Varying Systems Using Real-time Instrumental Variable Method

Evaluation: 

Midterm and Final Exams: 20-40%

Assignments and Regular Performance: 40-80%