Module Handbook

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Module EIT-AUT-453-M-7

Methods of Soft Control (M, 3.0 LP)

Module Identification

Module Number Module Name CP (Effort)
EIT-AUT-453-M-7 Methods of Soft Control 3.0 CP (90 h)

Basedata

CP, Effort 3.0 CP = 90 h
Position of the semester 1 Sem. in WiSe
Level [7] Master (Advanced)
Language [EN] English
Module Manager
Lecturers
Area of study [EIT-AUT] Automation Control
Reference course of study [EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering [2010]
Livecycle-State [NORM] Active

Courses

Type/SWS Course Number Title Choice in
Module-Part
Presence-Time /
Self-Study
SL SL is
required for exa.
PL CP Sem.
2V EIT-AUT-453-K-7
Methods of Soft Control
P 28 h 62 h - - PL1 3.0 WiSe
  • About [EIT-AUT-453-K-7]: Title: "Methods of Soft Control"; Presence-Time: 28 h; Self-Study: 62 h

Examination achievement PL1

  • Form of examination: written exam (Klausur) (90 Min.)
  • Examination Frequency: each semester

Evaluation of grades

The grade of the module examination is also the module grade.


Contents

  • Fuzzy control: fuzzy sets and operators, membership function, fuzzification, fuzzy implication, defuzzification, basic working principle of fuzzy controllers, typical applications.
  • Artificial intelligence and its application to control, modelling and diagnosis: supervised learning, unsupervised learning, Back-Propagation algorithm, radial basis function network, self-organizing map, support vector machine, reinforcement learning, training and validation of artificial neural networks, typical application scenarios.
  • Genetic algorithms and evolutionary algorithms: stochastic optimization approaches, selection of parameters, application in optimization, application in modelling, control and diagnosis.

Competencies / intended learning achievements

After completing this module you can...
  • ... explain the difference between classical control methods and computational intelligence based control methods (such as fuzzy control, artificial intelligence, genetic and evolutionary algorithms).
  • ... summarize and explain computational intelligence based control methods and explain their basic ideas, the advantage and the disadvantages.
  • ... identify typical application areas of these approaches.
  • ... apply these methods to solve specific problems in modeling, control and diagnosis.

Requirements for attendance of the module (informal)

Modules:

Requirements for attendance of the module (formal)

None

References to Module / Module Number [EIT-AUT-453-M-7]

Course of Study Section Choice/Obligation
[EIT-88.781-SG#2010] M.Sc. Electrical and Computer Engineering [2010] [Compulsory Modules] Specialization Modules [P] Compulsory
[EIT-88.A20-SG#2021] M.Sc. European Master in Embedded Computing Systems (EMECS) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.C48-SG#2021] M.Sc. Automation & Control (A&C) [2021] [Core Modules (non specialised)] A&C Core Courses [P] Compulsory
[EIT-88.D55-SG#2021] M.Sc. Embedded Computing Systems (ESY) [2021] [Free Elective Area] Elective Subjects [W] Elective Module
[EIT-88.781-SG#2021] M.Sc. Electrical and Computer Engineering [2021] [Section (non-specific)] Major Automation & Control (AUT) [P] Compulsory