Course content

Fuzzy Logic and Digital Control

Code:

18626

Abbreviation:

NEDIUP

Higher education institution:

Faculty of Mechanical Engineering and Naval Architecture

ECTS credits:

4.0

Load:

10(E) + 5(E) + 30(L)

Issuing teachers:

prof. dr. sc. Joško Deur

prof. dr. sc. Josip Kasać

Course contractors:

prof. dr. sc. Josip Kasać (L)

prof. dr. sc. Joško Deur (L)

Course description:

Course objectives: Overview of applications of digital control systems as well as fuzzy logic control systems to control of technical systems. Enrolment requirements and required entry competences for the course: "Basics of automatic control" Student responsibilities: Teaching activities consist of lectures and auditory and laboratory exercises, wherein student attendance is regularly checked. A minimum attendance rate of 70% is strictly enforced. Unethical behavior during preparation of the written assignment (equivalent to written exam) is not tolerated. Grading and evaluation of student work over the course of instruction and at a final exam: Successful completion of written assignment is equivalent to a successful written exam. Maximum proportions of individual exam components: Written assignment 50% Oral exam 50% Grading system: 5 87% or more, 4 76% to 87%, 3 65% to 76%, 2 50% 65%, 1 below 49% Methods of monitoring quality that ensure acquisition of exit competences: At the beginning of each lecture or exercise, a brief recapitulation of previously presented subject matter is performed (up to 5 minutes). During the lectures/excercise interactive teaching process is established. Afterwards, students are pointed towards further broadening the subject matter via appropriate literature. The subject matter for the next lecture/exercise is announced and additional teaching materials are offered. Consultations, either in person or via email correspondence, are also encouraged. Interaction with students during practical teaching activities is important for teaching process evaluation. Upon successful completion of the course, students will be able to (learning outcomes): analyze discretetime (digital) control systems build discretetime PID controller and statevariable controllers and estimators validate the performance of discretetime control systems with respect to reference and load disturbance recommend adaptive control system structures select appropriate fuzzy process model for control purposes create TakagiSugeno fuzzy controller design fuzzy controllers with and without explicit rule base recommend appropriate fuzzyneural network structure Lectures 1. Shannon"s theorem. Transformation in zdomain. Discrete transfer functions in zdomain. 2. Stability properties of the digital systems. The speed of the response and damping properties. 3. Design of the timediscrete PID controller. 4. Timedelay digital controllers. 5. Digital control by employing statespace mrthod. 6. Poleplacement method and optimal digital controller. 7. The basics of the identification of digital systems. 8. Fuzzy sets and procedures of fuzzyfications and defuzzyfications. 9. Design of the fuzzy logic controllers. Analysis of decision procedures. 10. Fuzzy logic controllers of types P and PI. 11. Fuzzy logic controller of PD type. 12. Adaptive fuzzy logic controller. 13. Design of an analytic fuzzy logic controller without fuzzy rule base. 14. Fuzzy logic controller of a robot with four degree of freedom. 15. Combinations of fuzzy logic and neural network systems. Exercises 1. The basics of the digital control. 2. Digital control simulation by using program tools from Matlab + Simulink. 3. Synthesis of the timediscrete PID controller. 4. Synthesis of the digital PID controller for DC motor position control. 5. Synthesis of digital control systems using state space method. 6. Synthesis of digital controller by using poleplacement method. 7. Identification of the dynamics of the car motor. 8. Examples of fuzzy sets. 9. An overview of design procedures of the fuzzy logic controllers. 10. Computer applications in the procedures of design of P and PI fuzzy logic controllers. 11. Examples of design of fuzzy logic controllers of PD type. 12. Tuning procedures of adaptive fuzzy logic controllers. 13. Examples of design of analytic fuzzy logic controllers without fuzzy rule base. 14. Computer simulations of applications of fuzzy logic controller to control of a robot with four degree of freedom. 15. An overview of the combinations of fuzzy logic and neural network systems.

Course languages:

Hrvatski

Mandatory literature:

1. Isermann, R.: Digital Control, Springer-Verlag, 1989.

2. Aström, K. J., Wittenmark, B.: "Computer Controlled Systems", Prentice-Hall, London, 1997.

3. Đonlagić, D.: Osnove projektiranja neizrazitih regulacijskih sustava, KoREMA, 1994.

4. Novaković, B.: Adaptive Fuzzy Logic Control Synthesis without a Fuzzy Rule Base, in Fuzzy Theory Systems, ed. by Leondes, C.T., New York, 1999.

5. Bishop, G., Welch, G.: "An Introduction to the Kalman Filter", course materials, University of North Carolina at Chapel Hill, 2001.

Recommended literature:

6. Pavković, D., Deur, J., Lisac, A., "A Torque Estimator-based Control Strategy for Oil-Well Drill-string Torsional Vibrations Active Damping Including an Auto-tuning Algorithm", Control Engineering Practice, Vol. 19, No. 8, pp. 836-850, 2011.

7. Pavković, D., Deur, J.: "Modeling and Control of Electronic Throttle Drive", Lambert Academic Publishing, Saarbrücken, Germany, 2011.

8. Deur, J., Ivanović, V., Pavković, D., Jansz, M.: "Identification and Speed Control of SI Engine for Idle Operating Mode", SAE paper #2004-01-898, SAE International, 2004.

Legend

E - Exercises

L - Lectures