2000 Volume 12 Issue 1 Pages 143-152
Recently, it has been proposed that there exists a variety of fuzzy logic systems which can incorporate numerical input-output pairs and linguistic information in a natural and systematic way. Those systems using nearest neighborhood clustering can be used to group the samples so that a group can be represented by only one rule, and have a function of same learning as neural network. A fuzzy logic systems using nearest neigborhood clustering were learned by optimal gains of PID control searched experimentally, and the three gains of PID control are changed by this learning processing. After there, when robot vehicle makes a straight drive with and without an angle of a road surface inclination, control methods that can continue to travel at constant speed in control subject are proposed in this paper. I saw that the advantages of those fuzzy logic systems have a small number of learning times, perform a one-pass operation on the training data, have a generalization ability and are computationally simple by comparing with and examining the results of speed control using neural network.
