Comparison and analysis of defuzzification methods of a fuzzy controller to maintain the cane level during cane juice extraction

Vyas, Deepali; Misra, Yogesh; Kamath, H. Ravishankar

doi:10.1109/spaces.2015.7058225

Cited by 6 publications

(1 citation statement)

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“…that can be accepted by the system that is controlled, this process is called defuzzification, there are several methods for this calculation, however in this work the centroid method was applied because of its low response time [30].…”

Section: If a T Hen B (4)mentioning

confidence: 99%

Experimental Development of Fuzzy Controllers for Thermal and Pneumatic Processes

et al. 2021

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In this project, a Fuzzy control system is proposed in an industrial process training module with two independent systems between them, one thermal and the other pneumatic. The control algorithm is developed in Python language v3.6 executed by a Raspberry Pi B+, both controllers depend on the error and change in error that are updated in times of 2 s and 1 s, for temperature and pressure respectively, communication with the plants uses A/D and D/A converters, the thermal Fuzzy was analyzed with three temperature references [50,100 and 150]°C, with a rise time of 191 s, 360 s and 505 s; steady state error of 5.5%, 0.7% y 0.7%, in the pneumatic system the speed of change between references is evaluated from 10 psi to 15 psi varying the activation of the compressor at the beginning of the experiments, the settling times obtained are 111 s and 106 s, with the compressor off the result is 116 s and 88 s, besides a maximum excess of 13% with inherent oscillations to the type system that are in an acceptable range.

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Section: If a T Hen B (4)mentioning

confidence: 99%