Course syllabus

Realtidssystem
Real-Time Systems

FRTN60, 7,5 credits, A (Second Cycle)

Valid for: 2021/22
Faculty: Faculty of Engineering, LTH
Decided by: PLED F/Pi
Date of Decision: 2021-04-23

General Information

Main field: Machine Learning, Systems and Control.
Elective Compulsory for: MMSR1
Language of instruction: The course will be given in English

Aim

The aim of the course is that the student should learn how to design and implement computer-based control systems.

Learning outcomes

Knowledge and understanding
For a passing grade the student must

be able to define the basic concepts of real-time systems
understand the advantages and disadvantages of different implementation techniques for real-time systems
understand how communication and synchronization is realized using semaphores, monitors, and messages
be able to describe the structure and workings of a real-time kernel
be able to design a computer-based control system using discretization of a continuous-time design and using sampling
be able to calculate the relations between discrete-time models on difference-equation form, transient responses, and pulse transfer functions
be able to calculate computer-based controllers on state-space form and PID form
understand how time-delays and jitter affect control performance
be familiar with how Grafcet, Petri nets, and state machines can be used in the analysis and implementation of event-based control systems
understand the problems associated with control over networks.

Competences and skills
For a passing grade the student must

be able to implement a computer-based control systems using concurrent programming techniques
be able to apply basic schedulability analysis for real-time systems.

Judgement and approach
For a passing grade the student must

understand the importance of formal methods in the design of safety-critical real-time systems
master teamwork and collaboration at the laboratory exercises.

Contents

A real-time system is characterized by the fact that it is not only the result of the calculation that matters, but also when the result is produced. Computers that are used for feedback control are good examples of real-time systems, since they must operate periodically in a time-scale that is dependent on the dynamics of the controlled process, while at the same time they must be able to respond to external events, often within a given time interval. Two types of examples are industrial control systems used in, e.g., process automation, and embedded control systems for, e.g. avionics, autonomous vehicles and robotics. The aim of the course is to study methods for design and implementation of real-time systems for control applications.

Introduction, Real-time programming, Synchronization and mutual exclusion, Real-time kernels and operating systems, Periodic controller tasks, Computer implementation of control algorithms, Discretization of continuous-time controllers, Sampling of continuous-time systems, Input-output models of discrete-time systems, Sequence control using Grafcet, Scheduling theory, Integrated control and scheduling, Implementation aspects, Control over networks.

Examination details

Grading scale: TH - (U,3,4,5) - (Fail, Three, Four, Five)
Assessment: Written exam (5 hours), three laboratory exercises. In the case of less than 5 registered students, the retake exams may be given in oral form.

The examiner, in consultation with Disability Support Services, may deviate from the regular form of examination in order to provide a permanently disabled student with a form of examination equivalent to that of a student without a disability.

Parts
Code: 0120. Name: Examination.
Credits: 6. Grading scale: TH. Assessment: Passed exam
Code: 0220. Name: Laboratory Work 1.
Credits: 0,5. Grading scale: UG. Assessment: Preparation exercises and approved participation in the laboratory
Code: 0320. Name: Laboratory Work 2.
Credits: 0,5. Grading scale: UG. Assessment: Preparation exercises and approved participation in the laboratory
Code: 0420. Name: Laboratory Work 3.
Credits: 0,5. Grading scale: UG. Assessment: Preparation exercises and approved participation in the laboratory

Admission

Assumed prior knowledge: Basic course in programming and FRTF05 Automatic Control, Basic Course.
The number of participants is limited to: No
The course overlaps following course/s: FRTN01, FRT031

Reading list

Årzén K-E: Real-Time Control Systems (latest edition) och Wittenmark, B, K.J. Åström och K.-E- Årzén: Computer Control: An Overview" (latest edition). Both are sold by KFS + additional material.

Contact and other information

Course coordinator: Karl-Erik Årzén, karl-erik.arzen@control.lth.se
Director of studies: Björn Olofsson, bjorn.olofsson@control.lth.se
Course administrator: Mika Nishimura, mika.nishimura@control.lth.se
Course homepage: http://www.control.lth.se/course/FRTN60
Further information: The course is only available for students admitted to the master's programme in Machine Learning, Systems and Control. All other students are referred to FRTN01 Real-Time Systems. This course is identical to FRTN01 Real-Time Systems except that the final project (2.5 hp) is missing.