Design of PID Disturbance Observer with Neuro Fuzzy Inverse Model for Precise Temperature Control in Infant Incubator

Utama, Yoga Alif Kurnia

doi:10.1109/icitamee50454.2020.9398509

Cited by 5 publications

(2 citation statements)

References 4 publications

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“…PID Control System (Proportional -Integral -Derivative controller) is a controller used to determine the precision of an instrumentation system with the characteristics of the presence of feedback on the system. [24] The PID control system consists of three control methods, namely P (Proportional), I (Integral), and D (Derivative) controls, each of which has advantages and disadvantages. In its implementation, each method can work alone or a combination of them.…”

Section: A Data Collectionmentioning

confidence: 99%

Comparative Analysis of PID and Fuzzy Temperature Control System on Infant Warmer

Majid

Setioningsih

Kholiq

et al. 2022

j.electron.electromedical.eng.med.inform

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Infant Warmer is a life support equipment that is used to provide heat relief for normal and premature babies who are unable to maintain their own body temperature when in a new environment. The purpose of this research is to design an infant warmer using the DS18B20 sensor to analyze the efficiency and effectiveness between PID and fuzzy temperature control. This study used temperature settings of 34 °C, 35 °C, and 36 °C. The tool used for reference from standard measurements uses a standard infant warmer. When using the PID control used in the microcontroller detects the set temperature difference value with the actual temperature, the difference in value will be input to the PID control. The error value becomes PWM input on the PID control, the PID control will process the error value and determine the output value for the heater, the temperature sensor will read the actual temperature value which will be compared again with the temperature setting, the PID control will continue to process the error value and determine the PWM output value. until the actual temperature is equal to the setting temperature or the error value is zero

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Section: A Data Collectionmentioning

confidence: 99%

Comparative Analysis of PID and Fuzzy Temperature Control System on Infant Warmer

Majid

Setioningsih

Kholiq

et al. 2022

j.electron.electromedical.eng.med.inform

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“…Fuzzy logic is a temperature control method that can be used to achieve this goal. Fuzzy logic combines Boolean logic and fuzzy mathematical principles, fuzzy sets enable more accurate decision-making in situations where membership is not completely certain or not clearly defined [8]. With fuzzy logic temperature control, the temperature can be adjusted automatically and quickly based on surrounding environmental conditions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Logic Temperature Control on Baby Incubator Transport Battery Efficiency

Co'o,

Wisana,

Kholiq

2024

j.teknokes

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Baby incubator transport is a life support tool used to maintain the body temperature of newborn babies during transportation from one place to another, such as from a hospital to an intensive care center with more complete facilities. The problem that often occurs in transport incubators is limitations in the power system. Baby incubator transport uses a battery as the main power source. However, the limited battery power can cause risks to the baby if there is a problem with the power system or the battery runs out. This study aims to monitor the remaining battery voltage in a transport baby incubator that uses fuzzy logic to control the temperature inside and will compare with the performance of PID control. This research uses a fuzzy logic method to control temperature and maximize battery power. In this study, researchers only looked at the efficiency of the fuzzy logic method in temperature control and the battery that will be used. The research uses a display that will display the battery voltage and current values, battery power percentage, skin temperature, chamber temperature, humidity and the selected temperature control. The module that has been made is then compared with the Digital Multimeter measuring instrument. From the results of data collection, the measurement of the remaining battery voltage between the sensor reading and the measuring instrument has a difference where at a temperature of 34 ºC it is 2.1%, at a temperature of 35 ºC it is 2% and at a temperature of 36 ºC it is 3.9%. When compared to research using PID control, fuzzy logic takes longer to reach the desired temperature and demands more battery power when compared to PID control.

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