Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Archives of Control Sciences

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Axial dispersion models and their basic properties

Bartova D., Jakes B., Kukal J.

Archives of Control Sciences

2009

Vol. 19, no. 1

5-22

The paper is oriented to summary of important basic relations, which characterize behavior of four axial dispersion models (AEO: axial enforced closed-open model, ACO: axial closed-open model, ACC: axial closed-closed model, AOO: axial open-open model) and three referential models (ideal mixed model, plug flow model, cascade of ideal mixers without back-mixing). Selected basic properties (parametric characteristics) of these models can be used for parameter identification of included hydrodynamic flow structure models. Mathematical description of models including initial and boundary conditions, transfer function, model transient response to Dirac impulse as weighting (impulse) function, model transient response to step function as step response are included in this study. There are also included further characteristics of impulse function: raw moments up to 4th order, variance, variation coefficient, skewness , kurtosis, location and value of mode. Complete set of these characteristics for all studied models is collected (model-by-model) in seven tables. The authors declare several properties of weighting function as key ones: value of 1st raw (dimensional) moment, parametric values and mode properties, related to dependence on Peclet number. The plots of parametric values and mode properties vs. Peclet number are mentioned in the paper for four studied axial dispersion models.

Lipschitz continuity of fuzzy controller

Bartova D., Kukal J., Majerova D.

Archives of Control Sciences

2006

Vol. 16, no. 1

19-35

Any traditional fuzzy controller performs a sequence of three processes: fuzzification, control algorithm and defuzzification. It is useful when the contoller exhibits continuous behavior with constrained output and sensitivity. After the normalization of controller inputs and outputs into the interval [0;1], we designed the fuzzy controller to be Lipschitz continuous, which implies the constrained sensitivity of the controller. Łukasiewicz algebra enriched by ŁAsqrt was used for the realization of the proposed fuzzy controller. The realization of fuzzification and control algotithm is trivial. The only problem is in the defuzzification. Neither Mamdani nor Larsen approaches are continuous in general. Both MOM and COG technoques generate discontinuous output behaviour. That is why we developed a new defuzzification method based on Łukasiewicz algebra. Thus, the proposed technique of defuzzification is based on propositional logic and it helps to realize a class of Lipschitz continuous fuzzy controllers. the controllers were realized in the Matlab environment.