Course syllabus adopted 2022-02-14 by Head of Programme (or corresponding).
Overview
- Swedish nameLogik, inlärning och beslutsfattande
- CodeSSY165
- Credits7.5 Credits
- OwnerMPSYS
- Education cycleSecond-cycle
- Main field of studyAutomation and Mechatronics Engineering, Electrical Engineering
- DepartmentELECTRICAL ENGINEERING
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language English
- Application code 35122
- Block schedule
- Open for exchange studentsYes
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0107 Examination 5 c Grading: TH | 5 c |
| |||||
| 0207 Laboratory 2.5 c Grading: UG | 2.5 c |
In programmes
- MPBME - BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
- MPPEN - PRODUCTION ENGINEERING, MSC PROGR, Year 2 (elective)
- MPSYS - SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory)
Examiner
Bengt Lennartson- Full Professor, Systems and Control, Electrical Engineering
Eligibility
General entry requirements for Master's level (second cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
Basic mathematical and programming skills. A basic knowledge in control engineering is recommended.Aim
The course aims to give fundamental knowledge and skills in the area of discrete event systems and especially modeling and specification formalisms, simulation, synthesis, optimization and control function implementation. Typical applications are control functions for embedded systems, control of automated production systems, and communication systems.Learning outcomes (after completion of the course the student should be able to)
* Use basic discrete mathematics in order to be able to analyze discrete event systems.* Give an account of different formalisms for modeling discrete event systems, especially finite state automata, formal languages, Petri nets, extended finite state automata, timed and hybrid automata, and demonstrate skill to choose between them.
* Present different kinds of specifications, such as progress and safety specifications, defining what a system should and should not do.
* Compute and analyze different properties of discrete event systems such as reachability, coreachability, and controllability.
* Give an account for the meaning of supervisor synthesis, verification, and simulation.
* Use computer tools in order to perform synthesis and optimization of control functions based on given system models and specifications of desired behavior for the total closed loop system.
* Formulate and analyze hybrid systems including discrete and continuous dynamics.
* Explain and apply basic Markov processes and queuing theory for performance analysis of systems including uncertainties.
Content
The course covers the following topics: Basic discrete mathematics, propositional logic, predicate logic, sets and operations on sets, relations, functions, abstract algebra and morphisms. Modeling and specification of logic and sequential behaviors. Examples of modeling formalisms include formal languages, finite automata, Petri nets, temporal logic, extended finite automata including variables, timed and hybrid automata. Model reduction based on bisimulation. Verification of safety and liveness properties, such as reachability, blocking, deadlock and forbidden states, through state-space search methods. Synthesis and optimization of control functions based on given system models and specification of desired behavior of the controlled system. Analysis of hybrid systems including discrete and continuous dynamics. Observers for non-deterministic systems including unobservable events. Markov processes and queuing theory for analysis of systems including stochastic uncertainties. Dynamic programming and reinforcement learning.Organisation
The course comprises lectures, exercises, and a number of assignments that address important parts of the course. These assignments involve modeling, specification, and synthesis and are to be handed in.Literature
Bengt Lennartson: Introduction to Discrete Event Systems - Lecture NotesExamination including compulsory elements
Written exam and passed hand-in-assignments.The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.