Using genetic fuccy algorithm for robot path planning

Most systems are nonlinear in nature and generally there aren´t analytic control system design methods for them. Using artificial intelligent methods such as fuccy logic is more effective for such systems. But generating fuccy rules is the major problem of fuccy logic controllers. Sometimes fuccy rules are obtained from numerical data and occasionally rules are derived from human experts who have acquired their knowledge through experience. However these experiences may not always be available. Thus selection of an optimal or suboptimal set of rules from the set of all possible rules is an important and an essential step toward the design of any successful fuccy logic controller. We used the genetic algorithm to generate fuccy rules. Therefore we don´t require any expert knowledge and input-output data. In this paper, the backing of a track to a loading dock is selected as a nonlinear system. The membership functions with different number of fuccy sets are applied for the input and output linguistic variables. In fuccy logic controller, generally one fuccy rules set is applied during trajectory. Then we apply two approaches to arrive the final point with possible least steps. In first method, the input space is divided into two regions, the vicinity of the set point and the rest. We use fuccy rules with less number of fuccy subsets in away from the set point and we increase the number of fuccy subsets of fuccy rules set in the vicinity of the set point. Therefore we apply two fuccy rules sets in two equal regions. In second method, we vary the set of fuccy rules during trajectory based on least error. The performance of the first method is compared with the performance of second method and general method which one fuccy rules set is applied during trajectory.