Hybrid Fuzzy Congestion Controllers for Computer Networks Tuned by Modified Particle Swarm Optimization

Karam, Zeyad A.

doi:10.11601/ijates.v7i2.250

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…The proposed controller outperforms on the AMWPID controller mention in [3] for the same case. While achieving an approximate value of hybrid fuzzy logic controller [22]. see Table IV.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers

Abdulkareem

Awad²

2020

Iraqi Journal of ICT

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The delay in delivering packets and packets loss in computer networks is due to the problem of congestion in TCP/AQM routers, and to solve this crisis the TCP and AQM worked together. Where TCP provides secure data transfer and designed to handle congestion after its occurrence. While AQM predicts congestion and tries to resolve the problem before it occurs. In this work, the Fuzzy-PID (FPID) controller presented to manage the avoidance of congestion problems associated with TCP networks. The approach is based on hybridization between the Fuzzy Logic Controller (FLC) and the Proportional-Integral-Derivative(PID) controller, with the structure of Fuzzy-Set-Point-Wight(FSW) for PID controller that optimized by Antlion Optimizer(ALO) Using a linearized TCP congestion model. our target is to control the queue length of the router for the queue level in demand. The FPID controller shows good robustness for a Different scenario of the TCP network and the queue length response showed fast tracking capability with good robustness to network parameters changing in comparison to PID controller, all simulations are carried out using MATLAB 2017b.

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“…The proposed controller outperforms on the AMWPID controller mention in [3] for the same case. While achieving an approximate value of hybrid fuzzy logic controller [22]. see Table IV.…”

Section: Discussionmentioning

confidence: 99%

Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers

Abdulkareem

Awad²

2020

Iraqi Journal of ICT

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“…The PD in FLC structure is chosen to achieve the minimized error in controller signal, that done by minimize the rise, peak, settling times and response overshoot. So, the proposed controller equation will be [21] and [22]:…”

Section: Fuzzy Controller Design and Simula-tion Of The Robot Kinematicsmentioning

confidence: 99%

Design, Implementation, Interfacing and Control of Internet of Robot Things for Assisting Robot

Karam¹,

Abdulkadhim²

2022

IJCDS

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Robotic arm plays a great role in assisting the human hand in current life. This project involves the design and implementation of two degrees of freedom (DOF's) assistant arm robot for aiding humans in the medical recovery for the upper left limb motion. The contribution in this work involves three parts: the first one, design special interfacing circuits based on (Arduino card) for controlling the designed arm motion, where the motion execution and record is done by designed reading and writing circuits, these circuits controlled by a designed MATLAB fuzzy proportional derivative (PD) controller, this control structure implemented by assisting of special Arduino Simulink library In MATLAB Simulink, where it will be used for control the signals transition, a new Simulink design proposed and investigated to interfacing the controller with the arm motors and encoders via the circuits. The second contribution is adding an assisting spring system to the arm first motor joint, where this addition rises the motion stability through the control phase by increasing the required torque in the arm first joint, especially after the human hand load is attached to the robot links, where its tested effectively without needing for replacing the motor with increased torque. The results illustrate the power of these circuits design and the error reduction occurred after supporting the system's spring. The Third contribution, An Internet of robotic thing (IoRT) model is designed, and then the robot arm is connected using our designed internet -based control system using IoT protocols for communication.

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“…For this purpose, using the above method and the membership function of Figures 4 and 5 we obtain the fuzzy rules, which you can see in the Tables 1, 2 and 3 [21,22,26].…”

Section: Rule I If E Is a E Is B Then K Is C K Is D Ismentioning

confidence: 99%

Bandwidth Management with Congestion Control Approach and Fuzzy Logic

Azami

Narm

2021

IJE

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Hybrid Fuzzy Congestion Controllers for Computer Networks Tuned by Modified Particle Swarm Optimization

Cited by 7 publications

References 9 publications

Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers

Fuzzy Set-Point Weight for PID Controller Based on Antlion Optimizer to Congestion Avoidance in TCP/AQM Routers

Design, Implementation, Interfacing and Control of Internet of Robot Things for Assisting Robot

Bandwidth Management with Congestion Control Approach and Fuzzy Logic

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