1. Course number and name: EEE703108 – Intelligent Control
2. Credit: 3 (Engineering Topics), including 30 hours of lectures. 60 hours of lab, and 90 hours of self-study; Required.
Contact Hours: 3 (Lecture: 2/week; Discussion & Examples: 1/week).
3. Instructor’s or course coordinator’s name: Ph.D. Huynh Ba Phuc.
4. Textbook:
a. Required:
[1] Nazmul Siddique, Intelligent Control: A Hybrid Approach Based on Fuzzy Logic, Neural Networks and Genetic Algorithms, 2014, Springer.
b. Additional Textbooks (Optional):
[2] Rudolf Kruse, Computational Intelligence A Methodological Introduction, 2nd Edition, 2016, Springer.
5. Specific course information:
a. Catalog description of the content of the course:
This course introduces students to the intelligent control architecture. It provides the skills and approaches to intelligent control. The content focuses on evolutionary algorithms applied in control techniques, including genetic algorithms and bacterial foraging algorithms. The course is delivered by incorporating specific practical examples in the lecture content.
b. Prerequisites: Robotics (MEM703039), Introduction to Object-Oriented Programming (CSE703029).
6. Specific goals for the course:
a. Course Learning Outcomes and Relationship to Student Outcomes:
|
At the end of the course, students will be able to |
Student Outcome No. |
|
LO.01 – identify complex intelligent control problems that can be solved by applying mathematical, scientific, and engineering principles. |
1 (1.1) |
|
LO.02 – solve complex intelligent control problems using engineering-based methods and tools. |
1 (1.3) |
|
LO.03 – establish a team that has clear goals and assign roles to all members that together provide leadership, without major faculty intervention. |
5 (5.1) |
|
LO.04 – distribute workloads and communicate effectively to create a collaborative environment that includes all members. |
5 (5.2) |
|
LO.05 – present experimental results in a required format that facilitates analysis and interpretation of data. |
6 (6.2) |
b. Related Student Outcomes:
|
No. |
The graduates must have: |
|
1 |
an ability to identify, formulate, and solve complex engineering problems by applying engineering, science, and mathematics principles. |
|
5 |
an ability to function effectively on a team whose members together provide leadership, create a collaborative and inclusive environment, establish goals, plan tasks, and meet objectives. |
|
6 |
an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to conclude. |
7. Brief list of lecture topics to be covered:
|
Week |
Lecture topics |
|
1, 2 |
Start-up Quiz Lesson 1: Introduction 1.1. Introduction to the course. 1.2. Intelligent control. 1.3. Intelligent control architecture. 1.4. Approaches to intelligent control. 1.5. About iterative learning control. 1.6. Programming languages and software tools. |
|
3 |
Lesson 2: Hybrid approaches 2.1. Evolutionary-Fuzzy Control. 2.2. Neuro-Fuzzy Control. 2.3. Evolutionary-Neuro Control. 2.4. Evolutionary-Neuro-Fuzzy Control. Homework 1. |
|
4, 5, 6 |
Quiz 1, Quiz 2 Lesson 3: Genetic Algorithms (GA) 3.1. Introduction. 3.2. Biological background. 3.3. Basic concepts. 3.4. Conventional optimization and search techniques. 3.5. Terminologies and operators of GA. 3.6. Advanced operators and techniques in GA. 3.7. Genetic programming. 3.8. GA optimization problems. 3.9. GA Implementation using Matlab. 3.10. Applications of GA. 3.11. Discussion of the final project. Homework 2, Homework 3. |
|
7, 8, 9 |
Quiz 3 Lesson 4: Bacterial Foraging Optimization (BFO) 4.1. Introduction. 4.2. Basic concepts in Bacterial Foraging Algorithm. 4.3. Applications. 4.4. Discussion of the final project. Homework 4. |
|
10 |
Quiz 4 Lesson 5: Practical discussion 5.1. Discussion on the practical experience. 5.2. Discussion on the final project. |
8. Brief list of lab topics to be covered:
|
Lab |
Topics |
|
1, 2 |
Presentation of an article on applying GA (Article 1). Report 1, Report 2. |
|
3, 4, 5 |
Write MATLAB program to repeat the work in Article 1. Report 3, Report 4, Report 5. |
|
6, 7 |
Presentation of an article on applying BFO (Article 2). Report 6, Report 7. |
|
8, 9, 10 |
Write MATLAB program to repeat the work in Article 2. Report 8, Report 9, Report 10. |
Each report gives up to 2 points.
9. Evaluation:
Scale: 0 – 10.
· Final score = CC1 (5%) + CC2 (5%) + LT (30%) + KTHP (60%).
· CC1: Attendance (5%)
· CC2: Participate in all Quizzes and discussion in the class (5%).
· LT: Quiz 1 (5%) + Quiz 2 (5%) + Homework 1 (5%) + Homework 2 (5%) + Report 1-5 (10%).
· KTHP: Quiz 3 (5%) + Quiz 4 (5%) + Homework 3 (5%) + Homework 4 (5%) + Report 6-10 (10%) + Final report (30%).
Students must pay attention to the deadlines of the assignments.
Detailed evaluation:
|
ASSIGNMENT |
RATING WEIGHT (%) |
||||
|
LO.01 |
LO.02 |
LO.03 |
LO.04 |
LO.5 |
|
|
Quiz 1 |
100 |
0 |
0 |
0 |
0 |
|
Quiz 2 |
100 |
0 |
0 |
0 |
0 |
|
Quiz 3 |
100 |
0 |
0 |
0 |
0 |
|
Quiz 4 |
100 |
0 |
0 |
0 |
0 |
|
Homework 1 |
60 |
0 |
0 |
0 |
40 |
|
Homework 2 |
60 |
0 |
0 |
0 |
40 |
|
Homework 3 |
60 |
0 |
0 |
0 |
40 |
|
Homework 4 |
60 |
0 |
0 |
0 |
40 |
|
Final Report |
20 |
30 |
20 |
20 |
10 |
|
Lab 1, Lab 2, Lab 6, Lab 7 |
50 |
0 |
0 |
0 |
50 |
|
Lab 3-5, Lab 8-10 |
0 |
100 |
0 |
0 |
0 |
10. Contribution of course to meeting the Professional Component:
Engineering Topics: 3 Credits (100%)
