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 purpose, various methods to tune their rules automatically have been proposed. In these method, some of them necessitate much time for its tuning, and the others are lacking in the generalization capability. In the fuzzy control by the steepest descent method, a use of piecewise linear membership functions (MSFs) has been proposed. In this algorithm, MSFs of the premise for each fuzzy rule are tuned having no relation to the other rules. Besides, only the MSFs corresponding to the given input and output data for the learning can be tuned efficiently. Comparing with the conventional triangular form and the Gaussian distribution of MSFs, an expansion of the expressiveness is indicated. As a result, for constructing the inference rules, the training cycles can be reduced in number and the generalization capability to express the behavior of a plant is expansible. An effectiveness of this algorithm is illustrated with an example of a parallel parking of an autonomous mobile robot.