Wall Following Control System with PID Control and Ultrasonic Sensor for KRAI 2018 Robot

Iqbal, Muhamad Hamam; Aji, Wahyu Sapto

doi:10.31763/ijrcs.v1i1.206

Cited by 5 publications

(5 citation statements)

References 8 publications

(10 reference statements)

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“…In addition, this device also employed C programming language with IDE software and is processed on Arduino Mega as Economical Control System in Power Consumption. Furthermore, another study was also carried out on PID temperature controller infant incubator using RTD, explaining that the PID algorithm method that controls PWM also control the heater [12] to keep the heater at the desired temperature setting. In addition, this device also used the PT100 RTD sensor.…”

Section: Introductionmentioning

confidence: 99%

“…This study aims to create and design an infant warmer using a DC heater equipped with a battery so that when there is a blackout from PLN, this device can still be used. However, this device has a weakness in the forms of excessive use of battery [12]. Another study also designed such device by using DHT11 sensor as the sensing element and the control method were actuated by lightbulbs.…”

Section: Introductionmentioning

confidence: 99%

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Infant Warmer with Digital Scales for Auto Adjustment PID Control Parameters

et al. 2022

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Babies need temperatures that match the temperature of the mother's womb, which is between 35°C – 37°C. The latest research on infant warmer device used fuzzy method as a system for controlling temperature in infant warmers. The problem raised in the previous research is that the temperature was not evenly distributed throughout the bed at each predetermined temperature setting. When it reached the setting temperature, the warmer continued to turn on so that the bed got hotter. Therefore, the purpose of the current research is to make an infant warmer device equipped with digital scales with a temperature setting of 350C- 370C using PID control to stabilize the temperature and ensure that the heat is evenly distributed on the bed. In addition, skin temperature is also added, allowing the nurses know at which level of patient's body temperature is when observations should be made. The infant warmer in this module used an arduino microcontroller which is displayed in 7 segments, the skin sensor used is the DS18B20 temperature sensor to read the skin temperature, while the infant warmer temperature sensor used is LM35 as a PID control system. The results of the current research in making the device module were compared with the measurement results of the comparator. It was revealed that current research has obtained smallest error of 0% in temperature setting of 350C. For the comparison with the incu analyzer, the smallest error was obtained at the temperature setting of 370C with an error value of 0% on the T5 measurement. Meanwhile, the difference in skin temperature against the thermometer is 0.10C. The results showed that the temperature distributed on the module had different error values. Hence, this research can be implemented on the PID control of infant warmer system to improve the performance of infant temperature stability.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Infant Warmer with Digital Scales for Auto Adjustment PID Control Parameters

et al. 2022

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“…The PID control system is a type of feedback control system that utilizes a controller called Proportional-Integral-Derivative (PID) [82], [83], [84], [85], [86], [87], [88], [89]. This type of control system is commonly used in the basic design of motor control systems [90], [91], [92], [93], [94], [95].…”

Section: Pid Controller Designmentioning

confidence: 99%

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

Chotikunnan

Pititheeraphab

2023

Journal of Robotics and Control

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This study aims to develop an expert system implementation of P controller and fuzzy logic controller to address issues related to improper control input estimation, which can arise from incorrect gain values or unsuitable rule-based designs. The research focuses on improving the control input adaptation by using an expert system to resolve the adjustment issues of the P controller and fuzzy logic controller. The methodology involves designing an expert system that captures error signals within the system and adjusts the gain to enhance the control input estimation from the main controller. In this study, the P controller and fuzzy logic controller were regulated, and the system was tested using step input signals with small values and larger than the saturation limit defined in the design. The PID controller used CHR tuning to least overshoot, determining the system's gain. The tests were conducted using different step input values and saturation limits, providing a comprehensive analysis of the controller's performance. The results demonstrated that the adaptive fuzzy logic controller performed well in terms of %OS and settling time values in system control, followed by the fuzzy logic controller, adaptive P controller, and P controller. The adaptive P controller showed similar control capabilities during input saturation, as long as it did not exceed 100% of the designed rule base. The study emphasizes the importance of incorporating expert systems into control input estimation in the main controller to enhance the system efficiency compared to the original system, and further improvements can be achieved if the main processing system already possesses adequate control ability. This research contributes to the development of more intelligent control systems by integrating expert systems with P controllers and fuzzy logic controllers, addressing the limitations of traditional control systems and improving their overall performance.

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“…There are many methods to select the PID/FOPID/NPID compensator parameters such as trial and error and the Ziegler-Nichols rule [37], [38], [39]. In this research, the parameters of PID/FOPID/NPID compensator are selected simultaneously with the MRAC parameters (adaptation rate and model reference coefficients) based on a new Covid-19 optimization technique.…”

Section: 𝑢(𝑡) = 𝐾 𝑝 [𝐾 𝑛1 (𝑒) 𝑒(𝑡)] + 𝐾 𝑖 ∫ [𝐾 𝑛2 (𝑒) 𝑒(𝑡)] 𝑑𝑡mentioning

confidence: 99%

Adaptive Controller with PID, FOPID, and NPID Compensators for Tracking Control of Electric – Wind Vehicle

Shamseldin

2022

Journal of Robotics and Control

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This paper presents a new combination between the Model Reference Adaptive Control (MRAC) with several types of PID’s controllers (PID, Fractional order PID (FOPID), and Nonlinear PID (NPID)) optimized using a new Covid-19 algorithm. The proposed control techniques had been applied on a new model for an electric-wind vehicle, which can catch the wind that blows in the opposite direction of a moving vehicle to receive wind; a wind turbine is installed on the vehicle’s front. The generator converts wind energy into electricity and stores it into a backup battery to switch it when the primary battery is empty. The simulation results prove that the new model of electric–wind vehicles will save power and allow the vehicle to continue moving while the other battery charges. In addition, a comparative study between different types of control algorithms had been developed and investigated to improve the vehicle dynamic response. The comparison shows that the MRAC with the NPID compensator can absorb the nonlinearity (air resistance and wheel friction) where it has a minimum overshoot, rise time, and settling time (35 seconds) among other control techniques compensators (PID and FOPID).

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Wall Following Control System with PID Control and Ultrasonic Sensor for KRAI 2018 Robot

Cited by 5 publications

References 8 publications

Infant Warmer with Digital Scales for Auto Adjustment PID Control Parameters

Infant Warmer with Digital Scales for Auto Adjustment PID Control Parameters

Adaptive P Control and Adaptive Fuzzy Logic Controller with Expert System Implementation for Robotic Manipulator Application

Adaptive Controller with PID, FOPID, and NPID Compensators for Tracking Control of Electric – Wind Vehicle

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