Objectives and competences
The objective of this course is that student will understand principles, design and use controllers based on soft-computer techniques.
Content (Syllabus outline)
• Introduction to the theory of soft-computing: definition, usage in control.
• Neural network control: artificial neuron, activation function, construction of multi-layer neural networks, different types of neural networks, BPG learning method, examples of robot control application.
• Control with genetic algorithms: biological basis of genetics algorithms, offspring, mutation, evolution, selection, Darwin algorithm, effect of cross-bread, effect of mutation, intersection of both effects (mutation and cross-bread), algorithm of genetic calculus, an application of optimisation problem in control of mechatronics system.
• Fuzzy logic control: fuzzy logic, fuzzification, defuzzification, fuzzy membership function, decision table, rule base. Application of fuzzy logic controller for mechatronics systems.
• Particle swarm optimisation (PSO) control: presentation of the PSO algorithm, comparison between PSO and genetic algorithms, aplication of PSO algorithm for syntesis of PID type of contoller for simple robot system, aplication of PSO algorithm for indentification of ASM parameters.
Learning and teaching methods
• lectures,
• tutorial,
• lab work.
Intended learning outcomes - knowledge and understanding
On completion of this course the student will be able to
• demonstrate knowledge and understanding from the topics of a design of controllers based on the soft-computing techniques,
• design, implement and use controller based on soft-computing techniques.
Intended learning outcomes - transferable/key skills and other attributes
Communication skills: oral lab work defence, manner of expression at written examination.
Use of information technology: use of software tools for design and programming of soft-computing techniques.
Readings
• R. Šafarič, A. Rojko; Inteligentne regulacijske tehnike v mehatroniki, Univerza v Mariboru,FERI, Maribor 2007
• A. Dobnikar: Nevronske mreže, teorija in aplikacije, Didakta, Radovljica 1990. S. Y. Kung: Digital neural networks, PTR Prentice Hall, Engelwood Cliffs, New Yersey,1993.
• Miran Brezočnik: Uporaba genetskega programiranja v inteligentnih proizvodnih sistemih, FS, Maribor, 2000.
• Elektronska skripta: Riko Šafarič, Andreja Rojko: Inteligentne regulacijske tehnike v mehatroniki, FERI, Maribor, 2005 (spletni naslov https://estudij.um.si/pluginfile.php/437200/mod_resource/content/1/Inteligentne%20regulacijske%20tehnike%20v%20mehatroniki%20V2.pdf).
Comments
Both midterm exams may be replaced by a written exam.
