A Fuzzy Inference LSI for an Automotive Control (Special Issue on ASICs for Automotive Electronics)

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Fuzzy control is suitable for automotive control, because fuzzy control achieves controllability as good as control by humankind. However, since automotive control requires milli-second response and learning control, and the fuzzy system in automobiles requires fewer components (built-in type), a custom fuzzy inference LSI is needed for automotive control. We then indicated requirements of a fuzzy inference LSI suitable for automotive control and fabricated a fuzzy inference LSI using 1.5 μm CMOS process technique. This fabricated fuzzy LSI is designed to utilize in various automotive control experiments such as engine control, cruise control, brake control and steering control. The number of input variables is six, the number of output variables is two, the maximum number of production rules is 256, and the inference time is 63 micro-seconds (under the condition of six inputs, two outputs and 256 rules). The features of the fuzzy LSI are high speed inference, a built-in type, learning control ability and a memory structure separating into a rule memory and a membership function memory. A fuzzy control system is implemented only by the addition of two devices: the fuzzy LSI and an EPROM. The fuzzy LSI was applied to a rough road durability test aiming at the automatic driving equivalent to the human driver operation. In the test, fuzzy control and linear control were compared in terms of the compensation steering degrees. Linear steering control had a high rate of compensation steering of less than thirty degrees. On the other hand, the accumulated steering compensation of less than twenty degrees in the fuzzy control was about one third that in the linear control. The fuzzy steering control had the same steering compensations as that of human steering. The fuzzy LSI fabricated for various experiments is too large (10.7 mm×10.9 mm) to adopt as automotive parts. Therefore, we studied a smaller-sized fuzzy LSI by limiting functions, by changing the parallel processing into sequential processing and by thinning out the memory data of input membership functions. The number of input variables is four, the number of output variables is two, the maximum number of production rules is 160 and the expected inference time is 140 micro-seconds (in the worst case). The obtained chip is small enough (4.8 mm×4.8 mm) for automotive applications. Since the chip contains all the memories that are needed to execute fuzzy inference, the chip can be built in a microprocessor as a fuzzy inference co-processor without any other circuits.
社団法人電子情報通信学会の論文
1993-12-25

A Fuzzy Inference LSI for an Automotive Control (Special Issue on ASICs for Automotive Electronics)

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