PWM Converter Based on Takagi-Sugeno Fuzzy Logic Inference for Internet of Things Systems

Abstract

The aim of the study is to apply fuzzy logic in IoT systems to estimate various environmental factors such as temperature, humidity, etc., and to convert them using the Takagi-Sugeno algorithm into a pulse-width modulation (PWM) signal to control motor speed, thermal power of heaters and other parameters in an energy efficient way. The paper explores the use of fuzzy data converters based on intelligent fuzzy inference systems in Internet of Things systems. An approach has been developed for converting fuzzified input parameters read from sensors in a PWM signal based on the Takagi-Sugeno fuzzy algorithm. Based on the proposed method, an IT-based smart fan was developed that supports a PWM signal. Therefore, the proposed fuzzy PWM converter method was used to simulate the operation of a fuzzy model using three parameters: temperature, relative humidity, and carbon dioxide (CO2) concentration in relation to the PWM signal. The proposed method demonstrates the ease of training a reasonable fan control system and enables efficient energy consumption.