FPGA implementation of high speed PI like Fuzzy control system for industrial automation applications
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2013, Vol 3, Issue 3
Abstract
Abstract: The modern digital control systems demand faster and robust calculation components for robotic and industrial automation applications. This type of elements are becoming more essential with the utilization of some new control algorithms, like the fuzzy control, the adaptive control, the sliding mode control, etc. The PID controllers are most widely used controllers in the industrial control systems. Fuzzy logic control presents a computationally efficient and robust alternative to conventional controllers for many systems. Although the traditional control methods which use mathematical models of systems to design an adequate controller, the fuzzy logic controllers use fuzzy deductions or inferences for the synthesis of controllers are powerful and robust.Digital controllers are implemented two styles; hardware based and software based. The software based implementation can be carried out on PC or any DSP processor. Such implementations will be inherently slower due to serial nature of the processor’s execution style. The FPGA platform carrying advantages of both ASIC and processor is more suitable option. On FPGA one can easily achieve higher speeds occupying only less area. In this project PI like fuzzy logic controller (PIFLC) will be implemented in VHDL for FPGA platform. This is a general purpose controller that can be used for different applications. This controller has three stages: the fuzzification, the inference and the defuzzification. The first component in the PIFLC is the fuzzifier that transforms crisp inputs into a set of membership values in the interval [0, 1] in the corresponding fuzzy sets. The knowledge base of the fuzzy controller consists of a database of linguistics statements (rules), which states the relationship between the input domain fuzzy sets and output domain fuzzy sets. Inference block implements this logic. The last step is the defuzzification and the final output is determined by weighted average of all contributions of the output sets
Authors and Affiliations
G Kamalesh
Keywords
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