2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 14 , Issue 1 , PP: 309-319, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ghufran abdulqader alhaddad 1 * , Anass yousif abass 2 , Nora Ahmed Mohammed 3
Doi: https://doi.org/10.54216/FPA.140123
The world has become more like a small community thanks to the internet, which connects millions of people, businesses, and pieces of technology for a variety of uses. Because of the significant influence these networks have on our lives, maintaining their efficiency is important, which necessitates addressing issues like congestion. In this study, PI-controller gains are adjusted using a variety of optimization strategies to regulate the nonlinear TCP/AQM model. This controller commits controlled pressured signaling characteristics and modifies computer network congestion. First manual tune PI-Controller are used; then several optimization techniques were used to tune PI-controller gains (Particle Swarm Optimization (PSO), Ant-Colony Optimization (ACO) and Simulated Annealing algorithm (SA)) and then Linear Quadratic Regulator theory are used. To test the reliability and effectiveness of each of the suggested controllers, several tests utilizing varied network parameter values, different queue sizes, and extra disturbances were conducted. MATLAB was used for all experiments., the results show the superiority of the LQR controller over PI controller with both manual and optimal tuning techniques.
AQM , network congestion control , LQR , PSO , ACO , SA , PI controller.
[1] E. Abharian and M. Alireza, "Hybrid GA-BF based intelligent PID active queue management control design for TCP network," in Proceedings of the 3rd International Conference on Electronics, Communications, and Technologies (ICECT), 2011.
[2] S. Jaineet and T. Maher, "Stability and Control of Network Congestion Control System," 2020.
[3] S. Sana and N. Thaker, "Particle swarm optimization-based LQ-servo controller for congestion avoidance," Iraqi Journal of Computers, Communications, Control & Systems Engineering (IJCCCE), vol. 19, no. 1, 2019.
[4] M. I. Berbek and A. A. Oglah, "Fuzzy Like PID Controller Based on SSO Design for Congestion Avoidance in Internet Router," Iraqi Journal of Computers, Communications, Control, and Systems Engineering (IJCCCE), vol. 21, no. 2, 2021.
[5] H. M. Kadhim and A. A. Oglah, "Congestion Avoidance and Control in Internet Router Based on Fuzzy AQM," Engineering and Technology Journal, vol. 39, part A, no. 2, pp. 233-247, 2021.
[6] R. P. Borase, D. K. Maghade, S. Y. Sondkar, and S. N. Pawar, "A review of PID control, tuning methods and applications," International Journal of Dynamics and Control, vol. 9, no. 2, pp. 818-827, 2021.
[7] O. Karahan, "Design of optimal fractional order fuzzy PID controller based on cuckoo search algorithm for core power control in molten salt reactors," Progress in Nuclear Energy, vol. 139, 103868, 2021.
[8] L. Wang, "PID control system design and automatic tuning using MATLAB/Simulink," John Wiley & Sons, 2020.
[9] X. Jin, K. Chen, Y. Zhao, J. Ji, and P. Jing, "Simulation of hydraulic transplanting robot control system based on fuzzy PID controller," Measurement, vol. 164, 108023, 2020.
[10] A. Stooke, J. Achiam, and P. Abbeel, "Responsive safety in reinforcement learning by PID Lagrangian methods," in Proceedings of the International Conference on Machine Learning (ICML), 2020, pp. 9133-9143.
[11] P. J. Gaidhane, M. J. Nigam, A. Kumar, and P. M. Pradhan, "Design of interval type-2 fuzzy precompensated PID controller applied to two-DOF robotic manipulator with variable payload," ISA Transactions, vol. 89, pp. 169-185, 2019.
[12] D. Wang et al., "Particle swarm optimization algorithm: an overview," Soft Computing, vol. 22, no. 2, pp. 387-408, 2018.
[13] X. Yang, J. Yuan, and H. Mao, "A modified particle swarm optimizer with dynamic adaptation," Applied Mathematics and Computation, vol. 189, no. 2, pp. 1205-1213, 2007.
[14] L. Jia and X. Zhao, "An improved particle swarm optimization (PSO) optimized integral separation PID and its application on central position control system," IEEE Sensors Journal, vol. 19, no. 16, pp. 7064-7071, 2019.
[15] H. Ali et al., "LQR/sliding mode controller design using particle swarm optimization for crane system," Al-Nahrain Journal for Engineering Sciences, vol. 23, no. 1, pp. 45-50, 2020.
[16] A. H. Alaidi, C. S. Der, and Y. W. Leong, "Systematic review of enhancement of artificial bee colony algorithm using ant colony pheromone," International Journal of Interactive Mobile Technologies, vol. 15, no. 16, p. 173, 2021.
[17] M. Dorigo and T. Stützle, "Ant colony optimization: overview and recent advances," Cham: Springer, 2019.
[18] B. Guan, Y. Zhao, and Y. Li, "An improved ant colony optimization with an automatic updating mechanism for constraint satisfaction problems," Expert Systems with Applications, vol. 164, 114021, 2021.
[19] Charchekhandra, B., " The Reading and Analyzing Of The Brain Electrical Signals To Execute a Control Command and Move an Automatic Arm", Pure Mathematics for Theoretical Computer Science, Vol 1, 2023.
[20] M. Paniri, M. B. Dowlatshahi, and H. Nezamabadi-pour, "Ant-TD: Ant colony optimization plus temporal difference reinforcement learning for multi-label feature selection," Swarm and Evolutionary Computation, vol. 64, 100892, 2021.
[21] Nadweh, R., " On The Fusion of Neural Networks and Fuzzy Logic, Membership Functions and Weights", Galoitica Journal Of Mathematical Structures and Applications, Vol 7, 2023.
[22] G. Crescenzio and C. Vito, "A simulated annealing algorithm for scheduling problems," Journal of Applied Mathematics and Physics, vol. 7, pp. 2579-2594, 2019.
[23] T. Mohmmed, "LQR controller design for stabilization of non-linear DIP system based on ABC algorithm," Eastern-European Journal of Enterprise Technologies, vol. 2, pp. 36-44, 2023.
