EE7551 DISCRETE EVENT SYSTEMS: MODELING AND CONTROL
| Course Code: | 5677551 |
| METU Credit (Theoretical-Laboratory hours/week): | 3 (3.00 - 0.00) |
| ECTS Credit: | 8.0 |
| Department: | Electrical and Electronics Engineering |
| Language of Instruction: | English |
| Level of Study: | Graduate |
| Course Coordinator: | Prof.Dr. KLAUS VERNER SCHMİDT |
| Offered Semester: | Fall Semesters. |
Course Objectives
Course Objective 1: Students will be able to model discrete event behavior using finite automata and regular languages
Student Learning Outcomes:
- Understand the concept of finite strings and regular languages
- Establish the relation between regular languages and finite state automata
- Understand the concept of state minimization and the canonical recognizer
Course Objective 2: Students will be able to apply supervisory control to discrete event systems
Student Learning Outcomes:
- Introduce controllable and uncontrollable events
- Understand the concept of controllability Understand the concept of nonblocking
- Develop the basic algorithm for nonblocking supervisory control
Course Objective 3: Students will learn how to exploit structural properties of discrete event systems by modular modeling and controller design
Student Learning Outcomes:
- Understand the concept of modular discrete event systems
- Introduce the concept of nonconflict among modular system components
- Learn about local modular control
Course Objective 4: Students will perform design complexity reduction by abstraction-based controller synthesis
Student Learning Outcomes:
- Understand the concept of abstraction
- Investigate the problem of loss of information after abstraction
- Learn conditions for avoiding loss of information
Course Objective 5: Students will be able to perform failure diagnosis for discrete event systems
Student Learning Outcomes:
- Understand basic principles of fault diagnosis for discrete event systems
- Learn about diagnosability and the diagnose automaton
- Study efficient algorithms for the diagnosability verification
Course Content
Introduction to discrete event systems, modeling, regular languages, finite state automata, state minimization , supervisory control loop, controllability, nonblocking, maximally permissive supervision, state attraction, optimal attraction, modular control, locally modular control, nonconflict, abstraction-based nonblocking verification, abstractiob-based supervisory control, natural observer, local control consistency, failure diagnosis, diagnosis automaton, diagnosability verification, analysis algorithms, synthesis algorithms.
Course Learning Outcomes
Program Outcomes Matrix
| Contribution | |||||
| # | Program Outcomes | No | Yes | ||
| 1 | Depth: Our graduates acquire in depth knowledge in one of the various specialization areas of Electrical and Electronics Engineering, they are informed about current scientific research topics and they implement innovative methods. | ✔ | |||
| 2 | Breadth: Our graduates get familiarized in other subspecialty areas related to their specialization in Electrical and Electronics engineering and/or relevant areas in other disciplines. | ✔ | |||
| 3 | Research: Our graduates acquire the skills to conduct and to complete scientific research by accessing contemporary knowledge in their specialty areas. | ✔ | |||
| 4 | Life-long learning: Our graduates develop their life-long learning habits. | ✔ | |||
| 5 | Communication skills: Our graduates concisely communicate their ideas and work related results in written and oral form. | ✔ | |||
| 6 | Ethics: Our graduates internalize rules of research and publication ethics as well as professional ethics. | ✔ | |||