Simple Implementation of Fuzzy Controller for Low Cost Microcontroller

Dwiono, Wakhyu; Taufiq, Arif Johar; Winarso, W

doi:10.1109/icaiit.2019.8834627

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…Another approach to accelerate the FLC computation process is using a lookup table. Authors in [31] generated a lookup table using Octave simulation. Results show that the generated lookup table is 7 times faster than the ordinary FLC.…”

Section: Related Work and Motivationmentioning

confidence: 99%

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

et al. 2022

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In this paper, an adaptable fuzzy control mechanism for an Unmanned Aerial Vehicle (UAV) to manipulate its mechanical actuators is provided. The mission (landing) for the UAV is defined to track (land on) an object that is detected by a deep learning object detection algorithm. The inputs of the controller are the location and speed of the UAV that has been calculated based on the location of the detected object.Two separate fuzzy controllers are proposed to control the UAV motor throttle and its roll and pitch over the mission and landing time. Fuzzy logic controller (FLC) is an intelligent controller that can be used to compensate for the non-linearity behaviour of the UAV by designing a specific fuzzy rule base . These rules will be utilized to adjust the control parameters during the mission and landing period in runtime.To add the effect of the ground for tuning the FLC membership function over the landing operation, a computational flow dynamic (CFD) modeling has been investigated. The proposed techniques is evaluated on MATLAB/Simulink simulation platform and real environment. Statistical analysis of the UAV location reported during stabilization and landing process, on both simulation and real platform, shows that the proposed technique outperforms the similar state-of-art control techniques for both mission and landing control.

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Section: Related Work and Motivationmentioning

confidence: 99%

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

et al. 2022

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“…In addition to the presence of amphipods, Amplysia sp was also found in nursery tanks. Amplysia sp is a mollusk that is a competitor in juvenile nurseries of sandfish [9]. Table 1 also shows that the highest percentage of survival was obtained by treatment C (90.62%) followed by treatment A (89.37%) and Treatment B (84.37%).…”

Section: Survival Ratementioning

confidence: 93%

“…All treatments decreased in every week gradually presumably because of the presence of amphipods in nursery tanks. Amphipod is a Crustacea filum that is a predator for juvenile sandfish that are raised [9]. The presence of Amphipods was due to the fact that the water used during nursery was only filtered using a sand filter and without using ultraviolet.…”

Section: Survival Ratementioning

confidence: 99%

“…It is also known that the survival rate is 50 -80% for juvenile sea cucumbers (length 2 -3 mm; weight < 1 g) which are nursery in the tub for three months and fed benthic [11]. Meanwhile, obtained a survival percentage of less than 80% for juvenile sea cucumber size 1 mm which were reared in ponds using the HAPA method for two months [9]. This indicates that the percentage of juvenile survival is largely determined by the handling method and the length of time for juvenile rearing.…”

Section: Survival Ratementioning

confidence: 99%

“…The high percentage of survival from all treatments was assumed to occur because of good feed intake and ideal juvenile stocking densities. The optimum stocking density for juvenile sandfish is 250-500 juveniles/m 2 [8,9]. Stocking densities still in the ideal range to cause competition for feed and space for activities is good so that the percentage of survival sandfish obtained is better [5].…”

Section: Survival Ratementioning

confidence: 99%

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Application of Sargassum sp extract on juvenile sandfish nursery

Tomatala,

Beruatwarin,

Haryadi

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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Sea cucumbers are a high-value fauna that can be found on Kei Island’s coastal area. Because of overexploitation and poor management, the sea cucumber population on Kei Island has declined dramatically. To solve this problem, the cultivation activities (breeding, nursery and growth out) of sea cucumbers need to be developed immediately. The aim of this study was to utilize the brown algae Sargassum sp to support the production of sandfish seeds in Household-Scale Hatchery. This study took place from April - May 2022 at Tual Teripang Center, Tual. Juveniles of sandfish are used as research objects with measuring between 3 - 15 mm in size and have a stocking density of 800 juveniles per tub. The feed used as treatment was included Sargassum sp extract 300 gr/tub (Treatment A), 400 gr/tub (Treatment B), and 500 gr/tub (Treatment C). Feeding has given every day and water changes was 50 - 60% per day. The calculation of growth and survival rate is the variable that observed in this study. As supporting data, water quality test measures were carried out. Three treatments have different effects on juvenile growth. The best result was obtained in Treatment C. For survival, showed all treatments had same effect on the survival rate of juvenile sandfish. It was concluded that Treatment C was better for the growth of juvenile sandfish than Treatment A and Treatment B.

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Implementation of fuzzy inference system algorithm in brooding system simulator with the concept of IoT and wireless nodes

Saputra

Rohmat

Najmurrokhman

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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This paper aims to implement the Fuzzy Inference System algorithm as a controller in an IoT-based observation system in a brooding system simulator. The system consists of sensor and actuator nodes that are connected to the internet using a microcontroller. The two nodes are connected wirelessly. Sensor nodes as devices that collect input data variables that are temperature and humidity. The value of the sensor readings is sent to Antares cloud storage by a microcontroller which functions as data storage so that it can be accessed by the user as well as an input controller. The Mamdani Fuzzy Inference System algorithm is implemented on the actuator node. The actuator node takes fuzzy input values from Antares and then outputs in the form of a PWM crisp value that aims to regulate blower speed and heating level. The experiments performed were comparing the calculation of the algorithm using MATLAB and the brooding system simulator. The results showed identical outputs with practical results with an average error of 0.11% for the PWM value of the blower and 0.22% for the PWM value of the heater. The scheme is expected to be an alternative solution for implementing the control algorithm on an IoT-based system.

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Simple Implementation of Fuzzy Controller for Low Cost Microcontroller

Cited by 5 publications

References 12 publications

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

An AI-in-Loop Fuzzy-Control Technique for UAV’s Stabilization and Landing

Application of Sargassum sp extract on juvenile sandfish nursery

Implementation of fuzzy inference system algorithm in brooding system simulator with the concept of IoT and wireless nodes

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