SYLLABUS
(Major in Automation)
1. Subject: INTELLIGENT CONTROL SYSTEMS
2.
Credits: 2 (Lecture 30 hours; Lab 15 hours)
3. Lecturer: Dr. HUỲNH THÁI HOÀNG ; Dr. NGUYỄN ĐỨC THÀNH; Dr. NGUYỄN
THIỆN THÀNH
4. Department: Automatic Control
5. Prerequisites:
6. Corequisites:
7. Subject objectives: To provide students with general knowledge about intelligent control
techniques and their applications in industry.
8. Subject description: The course discusses methods for the analysis and design of
intelligent control systems. The main topics include: general characteristics and structures
of intelligent control systems; methods for design knowledge-based control systems,
model-based control systems, intelligent adaptive control systems and learning control
systems using soft computing techniques such as neural networks, fuzzy logic and genetic
algorithm; foraging theory and applications in control; examples of intelligent control
systems in industry.
9. Contents:
9.1 CLASS LECTURES: 45 hours
Chapter
1
Topic
Introduction
1.1 Intelligent control concept
Hours
Ref.
6
[1],[2],
[3],[4]
6
[1],[2]
6
[1],[2]
1.2 General characteristics of intelligent control systems
1.3 Disciplines related to intelligent control
1.4 Neural networks
1.5 Fuzzy systems
1.6 Genetic algorithms
Direct Control
2.1 Fuzzy direct control
2.2 Fuzzy PID controller
2
2.3 Stability of fuzzy logic control system
2.4 Direct control using neural networks
2.5 Self turning of direct controllers using genetic
algorithms
Neural/Fuzzy Model Based Control
3.1 Neural/fuzzy models of nonlinear systems
3
3.2 Inverse control
3.3 Internal model control
3.4 Model reference control
3.5 Model-based predictive control
Adaptive control and learning control
4.1 Neural/fuzzy indirect adaptive control
4
6
[1],[2]
3
[2]
3
[1],[2],
[3],[4]
4.2 Neural/fuzzy direct adaptive control
4.3 Parameter learning control
4.4 Structure learning control
Control using foraging theory
5.1 Introduction to foraging theory
5.2 Cooperative foraging
5
5.3 Competitive foraging
5.4 Intelligent foraging
5.5 Optimization and control of robot team based on
foraging strategies
Intelligent control systems
6.1 Hierarchical structure of intelligent control systems
6
6.2 Basic elements of intelligent control systems
6.3 Examples of intelligent control systems in industries
6.4 Intelligent control research directions
9.2 LAB EXPERIMENTS: 15 hours
TT
Experiments
Hours
Lab
1
Fuzzy control of inverted pendulum
3
Digital Control and
System Engineering.
Building C6
2
Fuzzy control of magnetic levitation
system
3
Digital Control and
System Engineering.
Building C6
3
Seft tuning of fuzzy controller for
flexible link robot using genetic
algorithm
3
Digital Control and
System Engineering.
Building C6
4
Predictive control of a cascade tank
using neural networks/fuzzy model
3
Digital Control and
System Engineering.
Building C6
5
Fuzzy/neural adaptive control of a
helicopter model
3
Digital Control and
System Engineering.
Building C6
10. References:
Ref.
[1] Huỳnh Thái Hoàng
[2] Kelvin M. Passino
[3] Adrian A. Hopgood
[4] Panos J. Antsaklis,
Kelvin M. Passino (Eds)
Intelligent Control Systems, VNU-HCM Publisher, 2006 (in
Vietnamese)
Biomimicry for Optimization, Control and Automation,
Springer, 2006
Inteligent Systems for Engineers and Scientists, CRC Press,
2001
An Introduction to Inteligent and Autonomous Control,
Kluwer Acadamic Publisher, 1993.
11. Assessment:
No.
Assessment method
Times
Weight (%)
1
Mid-term examination
0
0
2
Experiment
1
20
3
Homework
4
40
4
Final examination
1
40
Head of the Department
Lecturer
(Full name and signature)
(Full name and signature)