Intelligent Control Systems (ETF AEI IU 5960) |
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General information |
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Module title | Intelligent Control Systems |
Module code | ETF AEI IU 5960 |
Study | ETF-B |
Department | Control and Electronics |
Year | 2 |
Semester | 3 |
Module type | Elective |
ECTS | 6 |
Hours | 60 |
Lectures | 34 |
Exercises | 20 |
Tutorials | 6 |
Module goal - Knowledge and skill to be achieved by students |
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| Course objective is to give students knowledge related to concepts and methods of artificial intelligence and its application for solving problems in control of dynamic systems. <br> |
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Syllabus |
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| 1. Introduction-Introduction to intelligent control systems. Definitions: Adaptation, Learning, Intelligence. Methodologies of application of artificial intelligence in control systems: adaptive control based on reference model (direct adaptive control), adaptive control based on self-tuning controller (indirect adaptive control), control based on learning. Aspects of problem modelling and presentation. Intelligent control system analysis. Technology, implementation and experimental evaluation. <br> 2. Machine learning, decision tree, Bayesian learning, Reinforcement learning. <br> 3. Expert systems in control systems. Knowledge presentation. Conclusion process. Expert control system. <br> 4. Fuzzy systems in control systems. Theoretical basics of fuzzy systems. Fuzzy systems application in control systems. Fuzzy control system stability. <br> 5. Artificial neural networks in control systems. Theoretical basics of artificial neural networks. Types of artificial neuron networks and their features. Application of artificial neural networks in control systems. <br> 6. Evolutionary algorithms in control systems. Theoretical basics of evolutionary algorithms. Application of evolutionary algorithms in control systems. <br> 7. Application and realisation examples. <br> |
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Literature |
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| Recommended | 1. Lecture notes and slides (will be available at the Web site). <br> 2. T. Michell: "Machine Learning", McGraw-Hill, 1997 <br> 3. M. Gupta, N. Sinha: "Intelligent Control Systems-Theory and Applications", IEEE Press, 1996 <br> 4. A. Zilouchian, M. Jamshidi: "Intelligent Control Systems Using Soft Computing Methodologies", CRC Press 2001 <br> |
| Additional | 1. R. Jager: "Fuzzy Logic in Control", TU Delft, 1995 <br> 2. T. Miller, R. Sutton, P. Werbos: "Neural Networks for Control", MIT Press, 1996 <br> 3. M. Jamshidi, L. dos Santos Coelho, R. Krohling, P. Fleming: "Robust Control Systems with Genetic Algorithms", CRC Press, 2003 <br> 4. L. Jain, N. Martin: "Fusion of Neural Networks, Fuzzy Sets and Genetic Algorithms-Industrial Applications", CRC Press, 1999 <br> |
Didactic methods |
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| Lectures. Individual and team work on project assignments in laboratory: by use of knowledge acquired through lectures and advanced development environment students gain experiences necessary for practical application of learned methods. Through seminar work students develop capability and skills of individual solving of given problems. <br> |
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Exams |
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| Through the course, student gains points by following system: <br> 1. Attendance to lectures and laboratory: 10 points; <br> 2. Homework: 10 points; <br> 3. First partial exam: 20 points; <br> 4. Second partial exam: 20 points; <br> 5. Final exam: 40 points. <br> |
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Aditional notes |
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