Particle Swarm Optimization based robust controller for congestion avoidance in computer networks

Author(s):  
Salih Qaraawy ◽  
Hazem Ali ◽  
Ali Mahmood

The network congestion is an essential problem that leads to packets losing and performance degradation. Thus, preventing congestion in the network is very important to enhance and improve the quality of service. Active queue management (AQM) is the solution to control congestion in TCP network middle nodes to improve theire performance. We design a linear quadratic (LQ)-servo controller as an AQM applied to TCP network to control congestion and attempt to achieve high quality of service under dynamic network environments. The LQ-servo controller is proposed to provide queue length stabilization with a small delay and faster settling time. The designed controller parameters are tuned by using the particle swarm optimization (PSO) method. The PSO algorithm was fundamentally applied to find the optimal controller parameters Q and R, such that a good output response could be obtained. The PI controller is examined for comparison reasons. The MATLAB simulation result shows that the controller is more effective than the PI in reaching zero steadystate error with better congestion avoidance under the dynamic network environment. Moreover, the proposed controller achieves a smaller delay and faster settling time


2020 ◽  
Vol 39 (4) ◽  
pp. 5699-5711
Author(s):  
Shirong Long ◽  
Xuekong Zhao

The smart teaching mode overcomes the shortcomings of traditional teaching online and offline, but there are certain deficiencies in the real-time feature extraction of teachers and students. In view of this, this study uses the particle swarm image recognition and deep learning technology to process the intelligent classroom video teaching image and extracts the classroom task features in real time and sends them to the teacher. In order to overcome the shortcomings of the premature convergence of the standard particle swarm optimization algorithm, an improved strategy for multiple particle swarm optimization algorithms is proposed. In order to improve the premature problem in the search performance algorithm of PSO algorithm, this paper combines the algorithm with the useful attributes of other algorithms to improve the particle diversity in the algorithm, enhance the global search ability of the particle, and achieve effective feature extraction. The research indicates that the method proposed in this paper has certain practical effects and can provide theoretical reference for subsequent related research.


Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory


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