SELF-TUNING OF FUZZY-REASONING BY THE STEEPEST DESCENT METHOD AND ITSAPPLICATION TO A PARALLEL PARKING

For a fuzzy control of manipulated variable so as to match a required output of a plant, tuning of fuzzy rules are necessary. For its purpos e, various methods to tune their rules automatically have been propose d. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the f uzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSF s of the premise for each fuzzy rule are tuned having no relation to t he other rules. Besides, only the MSFs corresponding to the given inpu t and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution o f MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduc ed in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illus trated with an example of a parallel parking of an autonomous mobile r obot.