Course Information
SchoolMechanical Engineering
Cycle / Level1st / Undergraduate
Teaching PeriodSpring
CoordinatorPanagiotis Seferlis
Course ID20000400

Programme of Study: UPS of School of Mechanical Engineering

Registered students: 32
OrientationAttendance TypeSemesterYearECTS
EnergyElective Course belonging to the selected specialization (Elective Specialization Course)1055
Design and StructuresCompulsory Course belonging to the selected specialization (Compulsory Specialization Course)1055

Class Information
Academic Year2019 – 2020
Class PeriodSpring
Faculty Instructors
Weekly Hours4
Class ID
Type of the Course
  • Scientific Area
Course Category
Specific Foundation / Core
Mode of Delivery
  • Face to face
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
  • English (Instruction, Examination)
General Prerequisites
Basic course in automatic control.
Learning Outcomes
Should be able to solve an optimal control problem using calculus of variations. Should be able to design a linear quadratic controller in the continuous and digital domain. Should be able to design an optimal state estimator and incorporate it in a control system. Should be able to design a linear and non-linear model-predictive controller.
General Competences
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Work in teams
  • Work in an interdisciplinary team
  • Appreciate diversity and multiculturality
  • Respect natural environment
  • Demonstrate social, professional and ethical commitment and sensitivity to gender issues
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
1. Overview of automatic control principles 2. Optimal control problem formulation Performance index selection – Constraints 3. Variational calculus in optimal control problems Unconstrained and constrained problems 4. Linear quadratic control Disturbance rejections and set-point tracking problems 5. Introduction to digital systems z-transform – digital transfer function Stability of digital systems – Digital PID 6. Control systems design in state space Controllability and observability State feedback – Observers and Kalman filters 7. Model predictive control Linear and non-linear systems Numerical solution and practical implementation
optimal control, linear quadratic control, model predictive control, optimal state estimation
Educational Material Types
  • Notes
  • Slide presentations
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Electronic form of the lecture notes. Sample controller computer programs.
Course Organization
Interactive Teaching in Information Center150.5
Student Assessment
Projects: 6 problem sets Evaluation: Project 100%
Student Assessment methods
  • Written Assignment (Formative, Summative)
  • Report (Formative, Summative)
Course Bibliography (Eudoxus)
Α. Vincent T. L. και W. J. Grantham, Μη Γραμμικά Συστήματα Αυτόματου Ελέγχου και Βέλτιστος Έλεγχος. Εκδόσεις Τζιόλα, 2001. Β. Παρασκευόπουλος Π. Ν., Βέλτιστος Έλεγχος, Φίλτρο Kalman, Στοχαστικός Έλεγχος, Αθήνα, 2004, σελ. 395, ISBN 960-91281-8-1. Γ. Καραμπετάκης Ν., Βέλτιστος έλεγχος, 2010.
Additional bibliography for study
1. Strengel R.F., Optimal control and estimation, Dover, 1994. 2. Zhou K., Robust and optimal control, Prentice Hall, 1996. 3. Goodwin G. C., Graebe S. F., Salgado M. E., Control System Design, Prentice Hall, 2001. 4. Παρασκευόπουλος Π.Ν., Αναγνώριση Συστημάτων και Προσαρμοστικός Έλεγχος, Αθήνα, 1992, σελ. 406, ISBN 960-91281-7-3. 5. Franklin G. F., J. D. Powell, Μ. Workman, Digital Control of Dynamic Systems, Prentice Hall, 2002. 6. Ogata K., Discrete-time Control Systems, Prentice-Hall, 1987.
Last Update