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The analog fuzzy intelligent controllers for autonomous mobile robot to avoid static and dynamic obstacles in its local environment are presented. The controller designed for the robot is reconfigurable in nature in terms of number of rules in database i.e. flexibility for online rule change as per the frequency of obstacle in the local environment. The controller is proposed with adjustable membership function in terms of shape and degree of overlapping with dynamic rule base. New accurate MAX and MIN circuits are introduced. The controller is simulated using Tanner® tool. The two-input single-output fuzzy controller with 25 rules is implemented in 0.25µm CMOS technology. The maximum delay was found to be 9.915ns for the processing of 25 rules and the value of FLIPS was found to be 100.85 MFLIPS.

This paper presents a high-speed VLSI fuzzy inference processor for the real-time applications using trapezoid-shaped membership functions. Analysis shows that the matching degree between two trapezoid-shaped membership functions can be obtained without traversing all the elements in the universal disclosure set of all possible conditions. A FPGA based pipelined parallel VLSI architecture has been proposed to take advantage of this basic idea, implemented on CycloneII-EP2C70F896C8. The controller is capable of processing fuzzified input. The proposed controller is designed for 2-input 1output with maximum clock rate is 12.96 MHz and 275.33 MHz for 16 and 8 rules respectively. Thus, the inference speed is 0.81 and 34.41 MFLIPS for 16 and 8 rules, respectively.