Předmět Fuzzy Control (MTI / FCR)

Na serveru studentino.cz naleznete nejrůznější studijní materiály: zápisky z přednášek nebo cvičení, vzorové testy, seminární práce, domácí úkoly a další z předmětu MTI / FCR - Fuzzy Control, Fakulta mechatroniky a MIS, Technická univerzita v Liberci (TUL).

Materiál	Typ	Datum	Počet stažení

Obsah

Lectures:1) Introduction: SC-Soft Computing, Neural networks, Genetic algorithm, references. Fuzzy sets and linguistic variables. Operations with fuzzy sets, membership function. Fuzzy logic.2) Inference rules, Mamdami and Larsen implication. Number of rules.3) Assign an output fuzzy set to a suitable value of sharp action variable -defuzzification. Method of the most plausible solution. Method of the best compromise-Center of Maximum, Fuzzy Logic Toolbox in MATLAB.4) Fuzzy controllers. General feedback control. Fuzzy controllers. The structure of fuzzy controller. Simple fuzzy PI and PD controllers.5) Design of the rule base. Design of the rule base-meta rules. Design of the membership function. Fuzzy PID controller based on the digital PI+D structure.6) Implementation of the fuzzy PI and PD controller in SIMULINK. Characteristic of fuzzy controllers.7) Tools for implementation of fuzzy control. Graphical User Interface (GUI). The FIS Editor, Membership Function Editor, The Rule Editor, The Rule Viewer, The surface Editor.8) Neural networks. Introduction - the brain. Neural networks in brain, neurons, dendrites, Cell body, Axon-outputs. Synaptic learning. Artificial neuron model.9) Perceptron learning rule. Classification task. Adaptive linear element (Adaline).10) Linear neural networks, artificial neural networks. Training of artificial network. Learning (delta) rule. Multi-layer feed-forward networks. Error back propagation. Gradient descent method.Practices:1) Introduction to fuzzy techniques. Illustrative examples: fuzzy sets, linguistic variable, degree of membership. Operations with fuzzy sets2) Fuzzy systems 1 - fuzzy composition, Inference rules, fuzzification methods, Illustrative examples - speed car3) Fuzzy systems 2 - comparison of defuzzification methods. Complex example - heat value of gas burner in dependence on the supplied oxygen, mathematical and graphical comparison Mamdani (Linguistic) fuzzy model vs. Singleton fuzzy model vs. Takagi-Sugeno fuzzy model4) Introduction to Fuzzy Logic Toolbox in MATLAB, Example - Fuzzy inference engine, Mamdani (max-min) Inference Algorithm5) Fuzzy modelling based on the expert knowledge, Input-output data, using white box model, Illustrative examples: tank filling, fuzzy model of student knowledge6) Fuzzy control - structure of fuzzy controllers, comparison of conventional vs. fuzzy control, example application - P, PI, PID fuzzy controller7) Fuzzy controller design for laboratory tasks 18) Fuzzy controller design for laboratory tasks 29) Summary example: Fuzzy controller system - port crane. Mathematical and physical analysis - nonlinear description of differential equations. The choice of linguistic variables, membership functions a rule base.10) Summary example: Fuzzy controller system - port crane. Fuzzy controller design - position of the load, the speed of the crane. Adding a pivot angle and its speed control. Implementation in Matlab fuzzy toolbox. Simulation results.

Získané způsobilosti

The students get basic informations about the foundations of fuzzy set theory from the mathematical point of view. Knowledge about the structure, operation and application of relational and functional fuzzy systems will be imparted. Seminar and practical training on PC consolidate the ability to develope and apply fuzzy algorithms.

Literatura

Wong, C-C; Chen, C-C. A clustering-based method for Fuzzy Modeling, Vol. E82-D, No. 6. IEICE Trans. Inf. & Syst., 1999. Drainkov, D. An Introduction to Fuzzy Control. Springer, 1996. Hellendoorn, H.; Reinfrank, M.; Driankov, D. An Introduction to Fuzzy-Control. Springer, Berlin, 1996. Hampel, R. Fuzzy Control - Theory and Practice. Physica, 2000. von Altrock, C. Fuzzy Logic (Vol. 1 - 3). Oldenbourg, 1993. Babuska, R. Fuzzy Modeling for Control. Kluwer Academic Publishers, 1998.

Požadavky

Requirements for getting a credit are activity at the practicals /seminars. Examination is of the writing and oral forms.

Garant

doc. Ing. Osvald Modrlák, CSc.

Vyučující

doc. Ing. Osvald Modrlák, CSc.doc. Ing. Osvald Modrlák, CSc.