University of West Attica

Artificial Intelligence and Data Science

Intelligent Control

Course info:

Semester: 6

Elective

ECTS: 6

Hours per week: 3

Professor: T.B.D.

Teaching style: Face to face, tutorials and project work

Grading: Final written exam (50%), Individual projects (50%)

Activity Workload
Lectures 26
Tutorials 13
Group work on Laboratory projects 48
Individual study 63
Course total 150

Learning Results

This course aims at introducing standard and advanced concepts and methods for automatic control. Emphasis is given on intelligent control techniques and how they can be used in conjunction with robust and adaptive control methodologies.

After successfully completing the course, the students will be able to:

  • Comprehend the concepts and basic principles of standard automatic control systems, as well as intelligent, robust and adaptive control systems
  • Develop and design automatic control systems based on neural networks, fuzzy logic and metaheuristic search methods
  • Develop and design model predictive control systems
  • Develop and design adaptive control systems

Skills acquired

  • Search, analysis and synthesis of data and information, using the necessary technologies
  • Individual work
  • Group work
  • Work in an multi-disciplinary environment
  • Decision making
  • Production of new research ideas
  • Creative and critical thinking
  • Introduction to automatic control
  • Mathematical modelling of dynamical systems
  • P, PI, PD, PID controllers
  • Fuzzy control
  • Tuning controller parameters using metaheuristic search methods
  • Neural control
  • Adaptive control
  • Reinforcement learning
  • Model predictive control

  1. Z.X. Cai, Intelligent Control: Principles, Techniques and Applications, World Scientific Publishing Company, 1998.

  2. Szederkényi, G., Lakner, R., Gerzson, M., Intelligent Control Systems: An Introduction with Examples, Springer, 2001

  3. Nazmul Siddique, Intelligent Control, Springer 2014

  4. Nguyen, H.T. Prasad, N.R., Walker, C.L. Walker E.A., A First Course in Fuzzy and Neural Control, Chapman and Hall/CRC, 2002.

  5. Omidvar , O., Elliott, D., Neural Systems for Control, Academic Press, 1997

  6. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. Second edition MIT Press 2018

  7. Jantzen, J., Foundations of Fuzzy Control: A Practical Approach, John Wiley & Sons, 2013

  8. E. F. Camacho, C. Bordons, Model Predictive Control, 2nd Edition, Springer, 2007

– Related scientific journals:

  1. IEEE Transactions on Cybernetics

  2. ΙΕΕΕ Transactions on Automatic Control

  3. Engineering Applications of Artificial Intelligence

  4. ΙΕΕΕ Transactions on Fuzzy Logic

  5. ΙΕΕΕ Transactions on Neural Networks and Learning Systems

  6. Intelligent Control and Automation

Learning Results - Skills acquired

Learning Results

This course aims at introducing standard and advanced concepts and methods for automatic control. Emphasis is given on intelligent control techniques and how they can be used in conjunction with robust and adaptive control methodologies.

After successfully completing the course, the students will be able to:

  • Comprehend the concepts and basic principles of standard automatic control systems, as well as intelligent, robust and adaptive control systems
  • Develop and design automatic control systems based on neural networks, fuzzy logic and metaheuristic search methods
  • Develop and design model predictive control systems
  • Develop and design adaptive control systems

Skills acquired

  • Search, analysis and synthesis of data and information, using the necessary technologies
  • Individual work
  • Group work
  • Work in an multi-disciplinary environment
  • Decision making
  • Production of new research ideas
  • Creative and critical thinking
Course content
  • Introduction to automatic control
  • Mathematical modelling of dynamical systems
  • P, PI, PD, PID controllers
  • Fuzzy control
  • Tuning controller parameters using metaheuristic search methods
  • Neural control
  • Adaptive control
  • Reinforcement learning
  • Model predictive control
Recommended bibliography

  1. Z.X. Cai, Intelligent Control: Principles, Techniques and Applications, World Scientific Publishing Company, 1998.

  2. Szederkényi, G., Lakner, R., Gerzson, M., Intelligent Control Systems: An Introduction with Examples, Springer, 2001

  3. Nazmul Siddique, Intelligent Control, Springer 2014

  4. Nguyen, H.T. Prasad, N.R., Walker, C.L. Walker E.A., A First Course in Fuzzy and Neural Control, Chapman and Hall/CRC, 2002.

  5. Omidvar , O., Elliott, D., Neural Systems for Control, Academic Press, 1997

  6. Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction. Second edition MIT Press 2018

  7. Jantzen, J., Foundations of Fuzzy Control: A Practical Approach, John Wiley & Sons, 2013

  8. E. F. Camacho, C. Bordons, Model Predictive Control, 2nd Edition, Springer, 2007

– Related scientific journals:

  1. IEEE Transactions on Cybernetics

  2. ΙΕΕΕ Transactions on Automatic Control

  3. Engineering Applications of Artificial Intelligence

  4. ΙΕΕΕ Transactions on Fuzzy Logic

  5. ΙΕΕΕ Transactions on Neural Networks and Learning Systems

  6. Intelligent Control and Automation