Using FPGA Technique for Design and Implementation of a fuzzy Inference System

Abstract

Recently, there has been increased use of fuzzy logic in control applications. The kernel of a fuzzy logic controller (FLC) is a fuzzy inference engine which includes the knowledge base, the decision making logic, and the defuzzification unit, as shown in Figure 1. For most of the reported applications, fuzzy inference algorithms of the FLCs were implemented in software and executed on a standard Von Neumann type processor or mucrocontroller. Although software-based FLCs are in general more economical and flexible, they often have difficulty in dealing with control systems that require very high processing and I/O handling speeds. For this reason, in the recent years, a number of hardware fuzzy inference systems have been reported or proposed [1–5, 7, 9–16] that reflect the diversity in fuzzy inference methods in technoloies related to hardware design. In general, analog hardware is relatively simple but lacks accuracy and reliability; on the other hand, digital hardware is accurate and reliable but is more complex and lacks speed if an iterative algorithm is unavoidable. In recent years, rapid advances in digital technology allow system designers to design custom computing machines based on a variety of technologies: full custom application-specific IC (ASIC), standard-cell, gate array, FPGA, programmable logic device (PLD), standard IC, etc.. Among these, FPGA is especially suitable for fast implementation and quick hardware verification. Besides technology alternatives, design decisions have to be made based on tradeoffs on performance, hardware resource anf flexibility. In general, higher performance can be achieved at the expense of more hardware resource and less flexibility.