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.

The ultrasonic wave is widely used for acquiring perceptual information necessary for indoor/outdoor navigation of mobile robots, where the system is implemented as a sound navigation and ranging system (sonar). A robot equipped with multiple ultrasonic sonars is likely to exhibit undesirable operation due to erroneous measurements resulting from cross-talk among the sonars. Each sonar transmits and receives a pulse-modulated ultrasonic wave for measuring the range and identifying its own signal. We propose a technique for generating pulse patterns for multiple concurrently operated ultrasonic sonars. The approach considers pulse-pattern generation as a combinatorial optimization problem which can be solved by a genetic algorithm (GA). The aim is to acquire a pulse pattern satisfying certain conditions in order to avoid cross-talk or keep the probability of erroneous measurement caused by cross-talk low. We provide a method of genotype coding for the generation of the pulse pattern. Furthermore, in order to avoid a futile search encountered when the conventional technique is used, we propose an improved genotype coding technique that yields considerably different results from those of the conventional technique.