A fuzzy logic-based stabilization system for a flying robot, with an embedded energy harvester and a visual decision-making system

Baba, Abdullatif; Alothman, Basil

doi:10.1016/j.robot.2023.104471

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…This system continuously measures the amount of slip that occurs while the tractor is tilling the land. The application of FL is also carried out on flying robots with a decisionmaking system based on aerial visual capture [13]. This flying robot has arms and can transport liquids.…”

Section: A Fuzzy Logic (Fl)mentioning

confidence: 99%

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

Wildan

2023

JATM

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Abstract—The development of modern technology has brought progress to the agricultural sector. Previously, farming was carried out using traditional methods, resulting in lower crop production. Now the world is faced with various problems, there are challenges such as climate fluctuations and increasing human population. This problem causes food needs to increase drastically, so adopting Industry 4.0 technology in the agricultural sector is necessary. Artificial Intelligence (AI) and Internet of Things (IoT) are part of industrial technology advances 4.0 that can be applied to modern agriculture. This paper reviews several AI technologies used in the agricultural sector, such as Fuzzy Logic (FL), Artificial Neural Network (ANN), Machine Learning (ML), Deep Learning (DL), Genetic Algorithm (GA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Support System (DSS). The application form of integration between AI and IoT is divided into several categories: soil monitoring, agricultural irrigation, fertilizer spraying, pest and plant disease control, harvesting, forecasting, and yield monitoring. This review paper was created to provide a comprehensive overview of modern agriculture integrating AI and IoT. This form of application makes it possible to predict the future of agriculture so that it can manage resources more efficiently and run autonomously. This review aims to analyze and explore the latest developments in integrating AI and IoT in agricultural equipment in the period 2019 to 2023. Thus, it is hoped that this article can provide in-depth insight into future agricultural technology advances. Keywords—Artificial Intelligence (AI), Internet of Things (IoT), Agriculture, Integration of AI and IoT, Smart farming.

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Section: A Fuzzy Logic (Fl)mentioning

confidence: 99%

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

Wildan

2023

JATM

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“…Next recognize the propeller clockwise and counterclockwise.Fig.5shows the two types of propellers clockwise (called thrusters) and counterclockwise (called pullers). Then it is mounted on a brushless motor according to the direction of rotation of the motor[13]. Next check the motor numbering with the Mission Planner Motor Test Menu[12] which is shown in Fig.6.…”

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confidence: 99%

PID Control Tuning Based on Wind Speed Sensor in Flying Robot

Hasan,

Akbar

2023

Control Syst. Optim. Lett.

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The problem that is often faced by flying robots when carrying out the Vertical Take Off Landing (VTOL) process is the lack of stability of the vehicle due to differences in wind speed at any time. This is because the PID that has been pre-tuned is the PID at a certain wind speed and it is possible that during the race the wind speed suddenly changes, causing the vehicle to be less stable in carrying out the mission. Therefore, this study proposes a PID Control Tuning Control system based on the Wind Speed Sensor. In experiments that have been carried out with anemometer readings of 1-5 m/s, the ideal tuning results are obtained with each parameter P_roll = 0.1453, I_roll = 0.0892, D_roll = 0.004, P_pitch = 0.144, I_pitch = 0.09, D_pitch = 0.004, P_yaw = 0.184, I_yaw = 0.0184, D_yaw = 0.00309. In experiments with anemometer readings of 6-10 m/s, the ideal tuning results were obtained with each parameter P_roll = 0.148, I_roll = 0.0905, D_roll = 0.004, P_pitch = 0.1444, I_pitch = 0.09, D_pitch = 0.004, P_yaw = 0.1867, I_yaw = 0.0181, D_yaw = 0.0037. In an experiment with an anemometer reading of 11-15 m/s, the ideal tuning results were obtained with each parameter P_roll = 0.1494, I_roll = 0.09, D_roll = 0.004, P_pitch = 0.1457, I_pitch = 0.0902, D_pitch = 0.004, P_yaw = 0.1894, I_yaw = 0.018, D_yaw = 0.0037. PID adjustment based on this anemometer sensor utilizes the latest real-time wind speed data to support the robot in order to overcome instability in certain wind conditions by tuning PID so that the vehicle can maintain stability when carrying out certain missions.

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Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

Cai,

Zhang,

Khadakar

et al. 2024

Complex Intell. Syst.

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Unmanned Aerial Vehicles (UAVs) have become important in an extensive range of fields such as surveillance, environmental monitoring, agriculture, infrastructure inspection, commercial applications, and many others. Ensuring stable flight and precise control of UAVs, especially in adverse weather conditions or turbulent environments, presents significant challenges. Developing control systems that can adapt to these environmental factors while ensuring safe and reliable operation is a main motivation. Considering the challenges, first, an adaptive model is identified using the input/output data sets. New adaptation laws are obtained for dynamic parameters. Then, a Type-3 (T3) Fuzzy Logic System (FLS) is used to compensate for the error of dynamic identification. T3-FLS is tuned by a sliding mode control (SMC) strategy. The robustness is analyzed considering the adaptation error using the SMC approach. The main idea is that the basic dynamics of UAVs are taken into account, and adaptation laws are designed to enhance the modeling accuracy. On the other hand, an optimized T3-FLS with SMC is introduced to eliminate the adaption errors and ensure robustness. Several simulations show that known parameters converge under uncertainty, and the stability is kept, well. Also, output signals follow the desired trajectories under dynamic perturbations, identification errors, and uncertainties.

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A fuzzy logic-based stabilization system for a flying robot, with an embedded energy harvester and a visual decision-making system

Cited by 6 publications

References 10 publications

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

A Review: Artificial Intelligence Related to Agricultural Equipment Integrated with the Internet of Things

PID Control Tuning Based on Wind Speed Sensor in Flying Robot

Automatic control of UAVs: new adaptive rules and type-3 fuzzy stabilizer

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