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All Journal International Journal of Robotics and Control Systems
Oudah, Manal Kadhim
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Control & Systems Engineering Electrical & Electronics Engineering

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Reduction of Large Scale Linear Dynamic MIMO Systems Using Adaptive Network Based Fuzzy Inference System Oudah, Manal Kadhim; Shneen, Salam Waley; Aessa, Suad Ali
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1684

Abstract

Large Scale Multiple Input Multiple Output (MIMO) technology is a promising technology in wireless communications, and it is already at the heart of many wireless standards. MIMO technologies provide significant performance improvements in terms of data transfer rate and reduction the interference. However, MIMO techniques face large-scale linear dynamic problems such as system stability and it will be possible to overcome this problem by tuning the proportional integral derivative (PID) in continuous systems. The aim of this paper is to design an efficient model for MIMO based on Adaptive Neural Inference System (ANFIS) controller and compare it with a traditional PID controller. and evaluated by objective function as integral time absolute error (ITAE). ANFIS is used to train fuzzy logic systems according to the hybrid learning algorithm. The training involves the fuzzy logic parameters through simulating the validation data to represent a model to know the correctness and effectiveness of the system. It is optimizes the system performance in real time, however, to avoid potential problems such as easy local optimality. In the proposed approach stability is guaranteed as the initial steady-state scheme. ITAE is combined with ANFIS to minimize the steady-state transient time responses between the high-order initial pattern and unit amplitude response. The proposed ANFIS self-tuning controller is evaluated by comparing with the conventional PID. MATLAB simulink is used to illustrate the results and demonstrate the possibility of adopting ANFIS controller. The simulation results showed that the performance of ANFIS controller is better than the PID controller in terms of settling time, undershoot and overshoot time.
Improving TCP/AQM Network Stability Using BBO-Tuned FLC Nadhim, Rasha F.; Oudah, Manal Kadhim; Aziz, Ghada Adel; Shneen, Salam Waley
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1761

Abstract

One of the modern technologies used to improve the performance of various systems, including communications networks and the Internet, is the technology based on biogeography (BBO) that many researchers in the field of automation and control have shed light on. Fuzzy logic is one of the expert systems that has dealt with its use in control systems by many researchers within different applications. The current work has shed light on the mechanism of using The Biogeography Based-Optimization (BBO) technique for adjusting FLC parameters is called (BBO-FLC). The simulation was performed using Matlab program and the researchers adopted the technique as part of the stability of TCP network. The performance of the techniques used in the optimization process can be identified by comparing the results of each case, such as the proposed technique, with other types represented by the traditional control type Proportional–integral–derivative controller (PID). The possibility of using modern and intelligent optimization techniques for the optimal controller is tested using a tuning process for the parameters of the fuzzy type expert controller with the help of the biogeography-based optimization (BBO) technique. The contributions of the research are to verify the possibility of improving the performance by comparing the behavior of the system for the proposed test and simulation cases by obtaining the prescribed level and without exceeding the permissible values.
Co-Authors Aessa, Suad Ali Aziz, Ghada Adel Nadhim, Rasha F. Salam Waley Shneen