Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Iswanto, Iswanto; Ahmad, Irfan

doi:10.18196/jrc.26142

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Fuzzy system is mainly used for control, such as controlling robot movement [44], [45], speed [46], and other factors [47][48][49]. However, fuzzy system can basically be used for anything.…”

Section: Adaptive Neuro Fuzzy Inference System Methodsmentioning

confidence: 99%

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Hakim

Habaebi

Toha

et al. 2022

Sensors

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In Wireless Sensor Networks which are deployed in remote and isolated tropical areas; such as forest; jungle; and open dirt road environments; wireless communications usually suffer heavily because of the environmental effects on vegetation; terrain; low antenna height; and distance. Therefore; to solve this problem; the Wireless Sensor Network communication links must be designed for their best performance using the suitable electromagnetic wave behavior model in a given environment. This study introduces and analyzes the behavior of the LoRa pathloss propagation model for signals that propagate at near ground or that have low transmitter and receiver antenna heights from the ground (less than 30 cm antenna height). Using RMSE and MAE statistical analysis tools; we validate the developed model results. The developed Fuzzy ANFIS model achieves the lowest RMSE score of 0.88 at 433 MHz and the lowest MAE score of 1.61 at 433 MHz for both open dirt road environments. The Optimized FITU-R Near Ground model achieved the lowest RMSE score of 4.08 at 868 MHz for the forest environment and lowest MAE score of 14.84 at 868 MHz for the open dirt road environment. The Okumura-Hata model achieved the lowest RMSE score of 6.32 at 868 MHz and the lowest MAE score of 26.12 at 868 MHz for both forest environments. Finally; the ITU-R Maximum Attenuation Free Space model achieved the lowest RMSE score of 9.58 at 868 MHz for the forest environment and the lowest MAE score of 38.48 at 868 MHz for the jungle environment. These values indicate that the proposed Fuzzy ANFIS pathloss model has the best performance in near ground propagation for all environments compared to other benchmark models

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Section: Adaptive Neuro Fuzzy Inference System Methodsmentioning

confidence: 99%

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Hakim

Habaebi

Toha

et al. 2022

Sensors

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“…The fuzzy rule was subsequently created to model the qualitative elements of human knowledge (reasoning based on experience) [30] and address the issue using those as its foundations. Even though fuzzy is typically employed for control [31], including robot movement [32], speed control [33], and many other [34], [35], however A fuzzy system can be applied to anything from detection [36] until forecasting [37]. In 1985, Takagi and Sugeno develop fuzzy model using implications and reasoning for industrial control [38].…”

Section: Adaptive Neuro Fuzzy Inference System (Anfis) Algorithmmentioning

confidence: 99%

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Hakim,

Habaebi,

Islam

et al. 2023

IEEE Access

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To facilitate wireless sensor networks deployment in dense jungle environments, the challenges of unreliable wireless communication links used for routing data between nodes and the gateway, and the limited battery energy available from the nodes must be overcome. In this paper, we introduce the Self-Organized Wireless Sensor Network (SOWSN) to overcome these challenges. To develop the traits needed for such SOWSN nodes, three types of computational intelligence mechanisms have been featured in the design. The first feature is the introduction of Multi Criteria Decision Making (MCDM) algorithm with simple Additive Weight (SAW) function for clustering the SOWSN nodes. The second feature is the introduction of the fuzzy logic ANFIS-optimized Near Ground Propagation Model to predict the wireless transmission link quality and power transfer between transmitters. The third feature is the introduction of the (Levenberg Marquardt artificial neural network (LM-ANN) for Adaptive Dynamic Power Control to further optimize the transmitter power levels, radio modulation, Spreading Factor configurations, and settings of the employed SOWSN LoRaWAN nodes based on predicted wireless transmission link quality parameters. The introduced features were extensively evaluated and analyzed using simulation and empirical measurements. Using clustering, near-ground propagation, and adaptive transmission power control features, a robust wireless data transmission system was built while simultaneously providing power conservation in SOWSN operation. The payload loss can be improved using SAW clustering from 1275 bytes to 5100 bytes. The result of power conservation can be seen from the reduction of transmission power in SOWSN nodes with the increase of transmission time (TOA) as its side effect. With the original power transmission at 20 dBm, original TOA time at 96.832 milliseconds for all nodes, and SNR 3 as input, transmission power was reduced to 12.76 dBm and the TOA increased to 346.78 milliseconds for all nodes.

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“…In conclusion we can use the fuzzy rule based on the reasoning for solving a real-world problem. Even though the fuzzy is usually used for control, such as robot movement [93], [94], speed [95], and other factors [96]- [98]. However, a fuzzy system can be used for anything.…”

Section: A Fuzzy Systemmentioning

confidence: 99%

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

Hakim

Septiyana

Suwarno

2022

Journal of Robotics and Control

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In this modern age, Internet of Things (IoT) and Wireless Sensor Network (WSN) as its derivatives have become one of the most popular and important technological advancements. In IoT, all things and services in the real world are digitalized and it continues to grow exponentially every year. This growth in number of IoT device in the end has created a tremendous amount of data and new data services such as big data systems. These new technologies can be managed to produce additional value to the existing business model. It also can provide a forecasting service and is capable to produce decision-making support using computational intelligence methods. In this survey paper, we provide detailed research activities concerning Computational Intelligence methods application in IoT WSN. To build a good understanding, in this paper we also present various challenges and issues for Computational Intelligence in IoT WSN. In the last presentation, we discuss the future direction of Computational Intelligence applications in IoT WSN such as Self-Organizing Network (dynamic network) concept.

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Second Order Integral Fuzzy Logic Control Based Rocket Tracking Control

Cited by 3 publications

References 28 publications

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments

Self-Organized Wireless Sensor Network (SOWSN) for Dense Jungle Applications

Survey Paper Artificial and Computational Intelligence in the Internet of Things and Wireless Sensor Network

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