Abstract
Based on the closed-loop particle swarm feedback model, this paper proposes a graphical method to analyze the stability of the computer network dynamic balance system. First, based on the second-order time delay system model of congestion control, the stability of the system is described by characteristic pseudopolynomials. Secondly, based on the inverse line, the stability of the system is verified by graphical analysis methods, and the PID controller parameter range that guarantees the stability of the system is obtained, and the relationship between the controller proportional gain boundary and the network characteristic parameters is analyzed. Then, based on the analysis of the basic particle swarm optimization algorithm, the particle swarm evolution formula is divided into two parts, its own factors and social factors, and the influence of each part on the evolution speed and position of the particle swarm is analyzed, and an improved particle swarm is proposed. Finally, according to the above analysis, we find the corresponding equation from the appropriate solution in turn, thereby designing a class of particle swarm optimization algorithm with fewer intermediate variables. In view of the system involved in the classical PID control parameter tuning method, the improved particle swarm algorithm is applied to the parameter tuning and optimization of the PID controller. During the experiment, the improved PSO-PID controller optimization algorithm was used in the random early detection algorithm of active queue management, the process of the improved algorithm was researched and designed, and the relevant performance of the improved algorithm was verified through simulation experiments.
Highlights
At present, Internet users are gradually increasing and network business traffic is becoming more and more complex. e continuous growth of network demand poses a very serious challenge to the load capacity of the network, so the network congestion problem has become increasingly severe [1]
Yousif [22] proposed a large time delay network congestion control algorithm based on an improved network model, taking into account the high-frequency component △(s) in the system model, analyzing the relationship between the characteristic roots of the closed-loop system and the characteristic polynomial coefficients, and designing PID controller parameters based on D stability domain. e researchers applied numerical optimization methods to obtain the controller parameters for the constraint conditions of variable time delays, so that the characteristic roots of the closed-loop system fell within the D stable region
A PI controller based on immune algorithm is designed to reduce the impact of time delay on NCS performance, and Smith predictor is used to compensate for random time delay to further reduce its impact on system performance. is method makes the PID controller have the ability to predict the network delay, and the PID parameters are adjusted online according to the predicted output error at the future time, which greatly improves the control performance of the system and has strong engineering significance [24,25,26,27]
Summary
Internet users are gradually increasing and network business traffic is becoming more and more complex. e continuous growth of network demand poses a very serious challenge to the load capacity of the network, so the network congestion problem has become increasingly severe [1]. Network congestion control is of great significance to the stability and fairness of network systems, and it is a very important part of communication network research. Congestion control is of great significance for ensuring the robustness of network systems and maintaining high service quality. It is an extremely active and important part of current communication network research and is an interdisciplinary research topic involving communication networks, computer science, and automatic control. E second is the particle swarm optimization and Broyden-Fletcher-Goldfarb-Shanno (BFGS) hybrid optimization algorithm, which combines the global search of the particle swarm optimization algorithm and the fast convergence characteristics of the BFGS method based on gradient optimization to improve the algorithm convergence speed and reduce the adverse effect on the system performance due to the integral saturation effect and the differential amplification effect on the noise in the traditional PID control Combining the advantages of integral separation PID and incomplete differential PID, the working principle of the improved PID controller is analyzed, and the algorithm implementation steps and parameter tuning methods are explained. e first is a particle swarm optimization algorithm based on chaotic search to optimize system parameters. is method is based on the ergodicity of system nodes to improve the efficiency of the algorithm. e second is the particle swarm optimization and Broyden-Fletcher-Goldfarb-Shanno (BFGS) hybrid optimization algorithm, which combines the global search of the particle swarm optimization algorithm and the fast convergence characteristics of the BFGS method based on gradient optimization to improve the algorithm convergence speed and reduce the adverse effect on the system performance due to the integral saturation effect and the differential amplification effect on the noise in the traditional PID control
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