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

2018 ◽  
Vol 7 (2) ◽  
pp. 17 ◽  
Author(s):  
Zeyad A. Karam
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Congestion Control ◽  
Fuzzy Logic Controller ◽  
Transmission Control Protocol ◽  
Particle Swarm ◽  
Peak Time ◽  
Queue Size ◽  
Queuing Delay ◽  
Swarm Optimization

One of the most debated issues nowadays is the quality of computer network service. The best internet service must provide a fast processing of the traffic. Each router has a queue of packets that provides a buffer space, where the packets wait for processing. Transmission Control Protocol (TCP) is a packets congestion control theory. Active Queue Management (AQM) is a mechanisms proposed to employ at gateways to improve the performance of TCP congestion control. AQM mechanisms aim to provide high link utilization with low loss rate and low queuing delay while reacting to load changes quickly. Random Early Detection (RED) is an extensively studied AQM algorithm that can detect congestion by dropping packets randomly with certain probability that serves as the function of the average queue size. In this work, hybrids Fuzzy Logic Controllers (FLC) are proposed to measure the router queue size directly by use them as a congestion controllers. A multiple hybrid fuzzy controllers are proposed, where (Proportional Integral Derivative controller (PID) -like FLC-Particle Swarm Optimization (PSO) Based, Proportional Derivative (PD)-like FLC with conventional I-PSO Based and PID tuned by Fuzzy Logic-PSO Based), which is provided to regulate the queue length, round trip time and packet loss. The Particle Swarmed Optimization (PSO) algorithm is used for tuning the gains of hybrid fuzzy logic controller which helps in reducing the error of the queue size. This is achieved through minimizing the rise time, peak time, settling time and overshoot of the AQM response. The empirical results revealed a high-performance improvement regarding the proposed method in comparison to previous works of other researchers.

scholarly journals The Optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on Particle Swarm Optimization Algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 99 ◽  
Author(s):  
Tien Tran
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Diesel Engine ◽  
Fuzzy Logic Control ◽  
Engine Speed ◽  
Fuzzy Logic Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Logic Control

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained.

scholarly journals Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Abroon Jamal Qazi ◽  
Clarence W. de Silva ◽  
Afzal Khan ◽  
Muhammad Tahir Khan
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Fuzzy Logic Controller ◽  
Ride Comfort ◽  
Particle Swarm ◽  
Active Suspension ◽  
Suspension System ◽  
Swarm Optimization ◽  
Quarter Model ◽  
Semiactive Suspension

This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.

Optimized Fuzzy Logic Controller for Semi-Active Control of Buildings Using Particle Swarm Optimization

2011 ◽  
Vol 255-260 ◽  
pp. 2505-2509 ◽  
Author(s):  
Mohammadreza Ostadali Makhmalbaf ◽  
Mohammad Amin Tutunchian ◽  
Masoud Zabihi Samani
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Active Control ◽  
Fuzzy Logic Controller ◽  
Particle Swarm ◽  
Engineering Structures ◽  
Swarm Optimization ◽  
Mr Dampers ◽  
Control Devices ◽  
Current Monitoring

Control devices can be used in structures to attenuate undesirable vibration on engineering structures. In order to mitigate the response of structures during the earthquakes and high intensity winds semi active control has been widely used. Semi-active control need less energy in compare with active control. So they are more reliable and more effective in mitigating the effects of earthquakes. MR damper are semi active control devices that are managed by sending external voltage supply. The inducing current monitoring of MR dampers are intelligently managed by fuzzy logic control (FLC). Validation of fuzzy logic controller that is optimized by a particle swarm optimization(PSO) is pursued in this study. Finally, optimal fuzzy logic controller is identified and validated through numerical simulation for seismic excitation. In 3-storey benchmark building, results showed that optimized fuzzy logic controller was robust and effective in reduction of both displacement and acceleration responses.

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