Adaptive neuro-fuzzy controller trained by genetic-particle swarm for active queue management in internet congestion

Abstract

Routers are vital during network congestion. All routers have input and output packet buffers. V<span lang="EN-US">Various congestion control strategies have been suggested. Some controller-based proportional-integral derivative (PIDs) have recently been offered as active queue management (AQM) solutions to alleviate the deterioration of transmission control protocol (TCP) congestion management system performance. However, the time delay is large, the data retention decreases, and oscillation occurs, suggesting that the present PID-controller is unable to fulfill quality of service (QoS) criteria. Some research is developed on new control technologies such as neural networks and fuzzy logic. This paper proposes the adaptive neuro-fuzzy inference system (ANFIS) like PID controller for AQM. This model employs genetic algorithms (GAs) and particle swarm optimization (PSO) to learn and optimize all variables for ANFIS like PID controller. Simulations were used to investigate the effects of using fuzzy like PID based on single sign-on (SSO), and (ANFIS like PI, ANFIS like PID with GA-PSO) controllers on the length of the queue for an AQM router, respectively. Then we compared the findings to see which approach should be utilized to manage the queue length for AQM routers. In simulations, ANFIS like PID has superior stability, convergence, resilience, loss ratio, goodput, lowest rising time, overshoot, and settling time.</span>

Dual stage cascade controller for temperature control in greenhouse

“…To start simulation for controlling temperature in greenhouse we must specify the desired temperature needed, in this study 25 o C is regarded as an optimal value for the greenhouse environment and based on this the controller with the smart GTO optimization method will track the and monitoring the response to reach to the desired value with a stable behavior, all simulation results is obtained using MATLAB 2019 and the parameter of the GTO algorithm is shown in Table 1. The system error between desired and actual response is monitored and minimized using ITAE function [26], [27] as shown in (18) in the smart GTO algorithm iterations, Figure 5 The system response is indicated in Figure 6 and for demonstrate the controller efficiency a comparison with two conventional controller (PI and PID) is done and the response of all controllers is shown in Figure 7, and all controllers gain values is shown in Table 2. The response analysis results for the comparison of all controllers are shown in Table 3.…”

“…PID controllers are classified as an easy and traditional controllers adopted for improving the performance of the system. Nowadays, many studies suggested different modifications and structures for more enhancement in system response either by combining PID controller with some intelligent methods such as neural network [17] or using fuzzy logic with it [18] or modify its structures based on system behavior as in [19], [20] or adopt the fractional analysis either the integral or the differential parts of PID controller or together by fraction number for improving system response, this form of enchantment is a special case controller and become an improved structure of conventional PID controller [21]. In this paper, a novel cascade structure named proportional-integral-one plus integral-derivative PI-(1+ID) [22] is suggested to control temperature based o GTO algorithm and its block diagram is shown in Figure 3.…”

Optimal backstepping controller for controlling DC motor speed

“…The ITAE function is considered as a fitness function to test the error continuously [27], the behavior of the ITAE fitness function for the optimal backstepping controller is is shown in Figure . 4. The system response using optimal backstepping controller is shown in Figure 5 and the gain of these two controllers is also tuned using SSA tuning algorithm and shown in Table 3, a comparison with two conventional controllers (PI &PID) is done to show the effectiveness of the proposed controller based on transient response analysis as indicated in Table 4.…”

Adaptive Synergetic Motion Control for Wearable Knee-Assistive System: A Rehabilitation of Disabled Patients

“…Another extension of this study could be to implement the proposed controller in a real-time environment, either using LabVIEW programming software or using other embedded hardware designs such as FPGA or Raspberry Pi [46]. Other control techniques could be suggested to conduct a comparison study for this medical application [47][48][49][50].…”