I-Cyber-Physical Systems

Course Code :2215FTICPS
Study domain:Electronics
Academic year:2019-2020
Semester:1st semester
Contact hours:90
Credits:9
Study load (hours):252
Contract restrictions: Exam contract not possible
Language of instruction:English
Exam period:exam in the 1st semester
Lecturer(s)Joachim Denil
Paul De Meulenaere
Jan Steckel

3. Course contents *

The course aims to introduce the students to the embedded design, verification and validation of complex cyber-physical systems, found in automotive, aerospace, railway, medical, etc. The course is divided into two main topics: (i) embedded systems design part and,(ii) advanced signal processing, decision making and control. 

In the embedded systems part of this course, students are introduced to the concepts of real-time and/or safety critical systems and their design. The theoretical sessions will explore different topics related to the design of such systems. We will base our analysis of these systems on the principles of modelling and simulation. After a thorough introduction to the modelling safety-critical CPS, real-time operating systems, based on fixed-priority and dynamic priority schedulers, are explored. Students will be able to design a schedule for their task-set based on the insights gained by schedulability analysis. Afterwards, other types of middlewares, that allow for more adaptability at run-time, are examined. 

Once the details of design and deployment of real-time systems are clear, the course will shift its focus to the verification, validation and accreditation of these systems. Topics such as model-in-the-loop and hardware-in-the-loop are used to verify system behavior. Special focus is also placed on functional safety and its impact on accreditation. 

The advanced signal processing, decision making and control part of the I-CPS course focusses on the design of the control and hardware of cyber-physical systems. First, students are introduced to computational architecture design choices and its impact on the system. Afterwards, the control and decision-making tool box of the students is increased with insights into the inner workings of state estimation, machine learning and model-predictive control. As this a broad overview of these techniques, we will focus on the general principles of the techniques and one concrete implementation of each. It is imperative that students know the limitation of such control and decision-making techniques. Finally, signal conditioning techniques are further elaborated on.