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FLC Logic Controller and Their Application on Automatic Container Crane System

FLC Logic Controller and Their Application on Automatic Container Crane System

OUTLINE

According to demand, this paper is compiled with two parts. In first part, we will discuss the basic theme and function of FLC along with its related terms. Second part will discuss the FLC application i.e. automatic container crane system. We will also include the schematic diagram of the system explaining input and output variables. We will also give its example on its I/O model to discuss its type i.e. PD, PI or PID.

PART I: FLC LOGIC CONTROLLER

FLC (FLC Logic Controller) is proved to successful in its application since two decades. Some these applications include operation of automatic container crane, control of water quality, control of an elevator, even its application in the nuclear reactor control and so on. These successful applications have captivated its reputation as an important tool. Its key applications are in those areas where plants face difficulties in deriving the proper mathematical models or facing limitations regarding performance along with conservative linear control schemes.

(Hasegawa 2007 349-352)FLC is control system that uses FLC logic. It's a mathematical system that analyze the i/p values of variables (logical) that manage the continuous values b/w 0 & 1, rather than digital or classical logic, which merely operates on the logical value of 0 &1 (On or Off) respectively.

Fuzzification

Crisp measurements can be obtained by using measurement devices in technical system, like 110.5 V or 31.5 °C. The first step is to transform these values into FLC sets (linguistic terms), commonly known as fuzzification. The relative functions of the linguistic variable speed will be mentioned below.

In the previous flow diagrams the extracted collection of rules constitutes the basic knowledge of the risk model. The description of uncertain heuristic knowledge is tackled by FLC logic in the improvement of Heuristic IF-THEN rules.

The membership functions were designed for all the variables that are considered in the flow diagram, while each operational problem must be defined properly by the FLC rule base or dot matrix. Fig. 5 shows the knowledge based model that works in order to obtain the settlement regarding microbiological related problems.

Mathematical concepts' fuzzification is generally based on the concepts of general characteristic function to membership functions. Let two FLC subsets be A and B of X. than its union (AUB) & intersection (AnB) can be defined as: (A n B) (x) = minimum (A(x), B(x)), (A ? B) (x) = maximum (A(x), B(x)) for all x ? X. E.g. multiplication (ab) can replace min (a,b). Minimum and Maximum operations provides a base for straightforward fuzzification, as in that case the FLC case extends more properties of traditional mathematics.

Introduction

It's a logical rule-based system encrypted in which horn clauses are used (i.e., IF-THEN rules). Knowledge based system actually stores these sorts of rules. Scalar values, that were fuzzified is the real FLC system input.

These proper set of rules are applied to input that is ...

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