FAQ >> A New Approach for Controlling Overhead Traveling Crane Using Rough Contr

A New Approach for Controlling Overhead Traveling Crane Using Rough Controller

This paper presents the idea of a rough controller with application to control the overhead traveling crane system. The structure of such a controller is based on a suggested concept of a fuzzy logic controller. A measure of fuzziness in rough sets is introduced. A comparison between fuzzy logic controller and rough controller has been demonstrated. The results of a simulation comparing the performance of both controllers are shown. From these results we infer that the performance of the proposed rough controller is satisfactory.

The crane can be considered as one of the most important tools used in industry to transfer loads from one desired position to another. Usually cranes have very strong structures in order to lift heavy payloads in factories, in building construction, on ships, and in harbors. Until recently, cranes were manually operated. But when cranes became larger and they are being moved at high speeds, their manual operation became difficult. Consequently, methods of automating their operation are being sought. Two special inference engines (two rule-base) FLC had been done with [1-2]. Many researchers [1-3] deal with the fuzzy controller, some of researchers took one FLC only to control two system’s variables like [3]. In many real processes, control relies heavily upon human experience. Skilled human operators can control such processes quite successfully without any qualitative models. The control strategy of human operator is mainly based on linguistic qualitative knowledge concerning the behaviour of an ill-defined process. In order to cope with this difficulty, the human mind using intuitive and subjective thinking is realized as fuzzy logic An alternative approach to manipulating incomplete or imprecise information was presented by Pawlak in (1982) as a rough set theory [4]. The essence of this approach relies on the approximation of incomplete or imprecise information by means of completely and precisely known pieces of information. As a natural need, Dubois and Prade, [5] combined fuzzy sets and rough sets in a fruitful way by defining rough fuzzy sets and fuzzy rough sets. By analogy with the concept of a fuzzy controller, the idea of a rough controller based on the notion of a rough set theory will be introduced in the next section.

As a conclusion, a rough controller works much faster than a conventional FLC under the same operating conditions. While controlling the system can be observed by using an FLC get a smooth control value as a function of time; applying a rough controller to get a sharp function of time for the control value. Nevertheless, the quality index does not differ very much for both controllers. However, the best error criterion P.I was obtained with FLC. The quickest running time was recorded with the rough controller (5 sec).

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