Reduction of Large Scale Linear Dynamic MIMO Systems Using Adaptive Network Based Fuzzy Inference System
(1) * Manal Kadhim Oudah Mail (University of Technology, Iraq)
(2) Salam Waley Shneen Mail (University of Technology, Iraq)
(3) Suad Ali Aessa Mail (University of Technology, Iraq)
*corresponding author

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.


Keywords


Multiple Input Multiple Output; Adaptive Neural Inference System; Proportional Integral Derivative Controller; Linear Dynamic Systems

   

DOI

https://doi.org/10.31763/ijrcs.v5i2.1684 		      

Article metrics

10.31763/ijrcs.v5i2.1684 Abstract views : 139 | PDF views : 26

   

Cite

How to cite item
   

Full Text

Download

References


[1] S. Edirisinghe, O. Galagedarage, I. Dias and C. Ranaweera, “Recent Development of Emerging Indoor Wireless Networks towards 6G,” Network, vol. 3, no. 2, pp. 269–297, 2023, https://doi.org/10.3390/network3020014.

[2] A. Q. Hameed, “Rayleigh Fading-Shadowing of Outdoor Channels Analysis Based on Snr-Pdf Model,” Kufa Journal of Engineering, vol. 10, no. 2, pp. 114-125, 2019, https://doi.org/10.30572/2018/KJE/100209.

[3] S. Gaikwad, and P. Malathi, “A Performance Analysis of Massive MIMO System using Antenna Selection Algorithms,” International Journal of Electrical and Electronics Research (IJEER), vol. 11, no. 1, pp. 126-131, 2023, https://doi.org/10.37391/ijeer.110117.

[4] S. Hussain, N. Bhadri, and M. S. Hussain, “Advancements in Wireless Communication,” International Journal of Electronics and Communication Engineering, vol. 7, no. 9, pp. 1-4, 2020, https://doi.org/10.14445/23488549/IJECE-V7I9P101.

[5] P. Kumar, “Investigation of the Effect of Multipath Fading on Wireless Communication Systems,” International Journal of Advanced Research in Engineering and Technology (IJARET), vol. 10, no. 2, pp. 822-829, 2019, https://doi.org/10.17605/OSF.IO/X8E53.

[6] K. Mahender, T. A. Kumar, and K. S. Ramesh, “Analysis of multipath channel fading techniques in wireless communication systems,” AIP Conference Proceedings, International Conference on Electrical, Electronics, Materials and Applied Science, vol. 1952, no. 1, p. 020050, 2018, https://doi.org/10.1063/1.5032012.

[7] H. A. Jeiad, R. J. S. Al-Bahadili, “Multipath Fading Effects on Uncoded and Coded Multiple Frequency Shift Keying Performance in Mobile Wireless Communications,” Journal of Engineering and Sustainable Development, vol. 23, no. 04, pp. 18-33, 2019, https://doi.org/10.31272/jeasd.23.4.2.

[8] M. A. Al-Ja'afari, S. H. Abdulwahed, and A. H. Ali, “Comparison Between MLD And ZF Algorithms for MIMO Wireless System at Rayleigh Channel,” Kufa Journal of Engineering, vol. 9, no. 4, pp. 13-22, 2018, https://doi.org/10.30572/2018/KJE/090402.

[9] T. Raj, R. Mishra, P. Kumar, and A. Kapoor, “Advances in MIMO Antenna Design for 5G: A Comprehensive Review,” Sensors, vol. 23, no. 14, p. 6329, 2023, https://doi.org/10.3390/s23146329.

[10] N. H. Jemaludin et al., “A comprehensive review on MIMO antennas for 5G smartphones: Mutual coupling techniques, comparative studies, SAR analysis, and future directions,” Results in Engineering, vol. 23, p. 102712, 2024, https://doi.org/10.1016/j.rineng.2024.102712.

[11] M. S. J. Singh, W. S. W. Saleh, A. T. Abed, and M. A. Fauzi, “A Review on Massive MIMO Antennas for 5G Communication Systems on Challenges and Limitations,” Jurnal Kejuruteraan, vol. l35, no. 1, pp. 9-103, 2023, https://doi.org/10.17576/jkukm-2023-35(1)-09.

[12] T. Younas et al., “Investigation of LS-MIMO systems with channel aging effects,” Physical Communication, vol. 40, p. 101088, 2023, https://doi.org/10.1016/j.phycom.2020.101088.

[13] Z. Wang et al., “Extremely Large-Scale MIMO: Fundamentals, Challenges, Solutions, and Future Directions,” IEEE Wireless Communications, vol. 31, no. 3, pp. 117-124, 2024, https://doi.org/10.1109/MWC.132.2200443.

[14] N. Mounika, M. S. L. Charita, V. G. Krishna, V. V. Vinay, U. A. S. Kumar, “Energy Efficiency of a Massive MIMO System,” International Journal of Engineering Research & Technology, vol. 9, no. 6, pp. 881-885, 2020, https://doi.org/10.17577/IJERTV9IS060536.

[15] N. Koppati, K. Pavani, D. Sharma, and P. K Sharma, “MIMO To LS- MIMO: A Road To Realization of 5G,” AIP Conference Proceedings, vol. 1859, no. 1, p. 020075, 2017, https://doi.org/10.1063/1.4990228.

[16] F. A. Dicandia, and S. Genovesi, “Spectral Efficiency Improvement of 5G Massive MIMO Systems for High-Altitude Platform Stations by Using Triangular Lattice Arrays,” Sensors, vol. 21, no. 9, p. 3202, 2021, https://doi.org/10.3390/s21093202.

[17] M. A. Albreem, M. Juntti and S. Shahabuddin, “Massive MIMO Detection Techniques: A Survey,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3109-3132, 2019, https://doi.org/10.1109/COMST.2019.2935810.

[18] A. Khoso, X. Zhang, A. H. Shaikh, “Low-complexity signal detection for large-scale MIMO systems with second-order Richardson method,” Wireless communications, vol. 59, no. 9, pp. 467-469, 2020, https://doi.org/10.1049/el.2020.0208.

[19] S. Jolania, R. Sindal, and A. Saxena, “A Systematic Review on Challenges in Massive MIMO Based 5G and Beyond Wireless Networks and Their Solutions,” ECTI Transactions on Electrical Engineering, Electronics, and Communications, vol. 21, no. 3, p. 251469, 2023, https://doi.org/10.37936/ecti-eec.2023213.251469.

[20] M. Q. Sulttan, S. W. Shneen, and J. M. D. Alkhasraji, “Performance enhancement of large-scale linear dynamic MIMO systems using GWO-PID controller,” Bulletin of Electrical Engineering and Informatics, vol. 12, no. 5, pp. 2852-2859, 2023, https://doi.org/10.11591/eei.v12i5.4870.

[21] Q. Hu, M. Zhang, and R. Gao, “Key Technologies in Massive MIMO,” 4th Annual International Conference on Wireless Communication and Sensor Network, vol. 17, no. 9, 2017, https://doi.org/10.1051/itmconf/20181701017.

[22] S. Kalantaievska et al., “Development of a complex mathematical model of the state of a channel of multi-antenna radio communication systems,” Eastern-European Journal of Enterprise Technologies, vol. 3, no. 9, pp. 21-30, 2019, https://doi.org/10.15587/1729-4061.2019.166994.

[23] X. Jiang, H. Wang, Z. Zhang, X. Gao, L. Dai and M. F. Iskander, “Low RF-Complexity Massive MIMO Systems Based on Vertical Spatial Filtering for Urban Macro Cellular Networks,” IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 9214-9225, 2017, https://doi.org/10.1109/TVT.2017.2711920.

[24] M. A. Haque et al., “Machine learning-based technique for directivity prediction of a compact and highly efficient 4-port MIMO antenna for 5G millimeter wave applications,” Results in Engineering, vol. 24, p. 103106, 2024, https://doi.org/10.1016/j.rineng.2024.103106.

[25] N. X. Chiem, B. X. Hai, and T. C. Phan, “Synthesis of Adaptive Sliding Mode Control for Twin Rotor MIMO System with Mass Uncertainty based on Synergetic Control Theory,” International Journal of Robotics and Control Systems, vol. 4, no. 1, pp. 174-187, 2024, https://doi.org/10.31763/ijrcs.v4i1.1307.

[26] N. H. Sahrir, and M. A. M. Basri, “Intelligent PID Controller Based on Neural Network for AI-Driven Control Quadcopter UAV,” International Journal of Robotics and Control Systems, vol. 4, no. 2, pp. 691-708, 2024, https://doi.org/10.31763/ijrcs.v4i2.1374.

[27] M. Patrascu, A. B. Hanchevici, I. Dumitrache, “Tuning of PID Controllers for Non-Linear MIMO Systems Using Genetic Algorithms,” IFAC Proceedings Volumes, vol. 44, no. 1, pp. 12644-12649, 2011, https://doi.org/10.3182/20110828-6-IT-1002.01116.

[28] A. Taieb, M. Soltani, and A. Chaari, “Parameter optimization of MIMO fuzzy optimal model predictive control by APSO,” Complexity, vol. 2017, no. 1, pp. 1-11, 2017, https://doi.org/10.1155/2017/5813192.

[29] S. Slama, A. Errachdi, and M. Benrejeb, “Research Article Neural Adaptive PID and Neural Indirect Adaptive Control Switch Controller for Nonlinear MIMO Systems,” Mathematical Problems in Engineering, vol. 2019, no. 1, pp. 1-11, 2019, https://doi.org/10.1155/2019/7340392.

[30] S. Zhong, Y. Huang, and L. Guo, “New Tuning Methods of Both PID and ADRC for MIMO Coupled Nonlinear Uncertain Systems,” IFAC-Papers Online, vol. 53, no. 2, pp. 1325-1330, 2019, https://doi.org/10.1016/j.ifacol.2020.12.1867.

[31] J. M. Alkhasraji, S. W. Shneen, and M. Q. Sulttan, “Reduction of Large Scale Linear Dynamic MIMO Systems Using ACO-PID Controller,” Ingeniería Investigación, vol. 44, no. 1, p. e106657, 2024, http://doi.org/10.15446/ing.investig.106657.

[32] A. Rospawan, C. -C. Tsai and C. -C. Hung, “Output Recurrent Fuzzy Broad Learning Systems for Adaptive MIMO PID Control: Theory, Simulations, and Application,” IEEE Access, vol. 12, pp. 19388-19404, 2024, https://doi.org/10.1109/ACCESS.2024.3359293.

[33] K. Bagadi, V. Annepu, N. Raju Challa, F. Benedetto, T. Shongwe and K. Rabie, “Enhancing Performance of Massive MU-MIMO System With LR-RTS: A Low-Complexity Detection Algorithm,” IEEE Access, vol. 12, pp. 97085-97098, 2024, https://doi.org/10.1109/ACCESS.2024.3422182.

[34] E. Elakkiyachelvan, R. J. Kavitha, “Dynamic channel estimation in large-scale massive MIMO systems with intelligent reflecting surfaces: Leveraging Khatri-Rao factorization and bilinear alternating least squares,” Ain Shams Engineering Journal, vol. 15, no. 11, p. 103043, 2024, https://doi.org/10.1016/j.asej.2024.103043.

[35] H. Abdelfattah, S. A. Kotb, M. Esmail, and M. I. Mosaad, “Adaptive Neuro-Fuzzy Self-Tuned-PID Controller for Stabilization of Core Power in a Pressurized Water Reactor,” International Journal of Robotics and Control Systems, vol. 3, no. 1, pp. 1-18, 2023, http://dx.doi.org/10.31763/ijrcs.v3i1.710.

[36] S. W. Shneen, M. Q. Sulttan, and M. K. Oudah, “Design and implementation of a stability control system for TCP/AQM network,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 22, no. 1, pp. 129-136, 2021, http://doi.org/10.11591/ijeecs.v22.i1.pp129-136.

[37] Z. B. Abdullah, S. W. Shneen, and H. S. Dakheel, “Simulation model of PID controller for DC servo motor at variable and constant speed by using MATLAB,” Journal of Robotics and Control, vol. 4. no. 1, pp. 54-59, 2023, https://doi.org/10.18196/jrc.v4i1.15866.

[38] S. A. Eissa, S. W. Shneen, and E. H. Ali, “Flower Pollination Algorithm to Tune PID Controller of TCP/AQM Wireless Networks,” Journal of Robotics and Control, vol. 4, no. 2, pp. 149-156, 2023, http://doi.org/10.18196/jrc.v4i2.17533.

[39] M. H. Jaber, M. K. Oudah, M. Q. Sulttan, S. W. Shneen, “BBO tuned PI controller for the stability of TCP networks,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 21, no. 2, pp. 264-271, 2023, http://doi.org/10.12928/telkomnika.v21i2.21795.

[40] A. A. Chlaihawi, “Genetic algorithm error criteria as applied to PID controller DC-DC buck converter parameters: an investigation,” IOP Conference Series: Materials Science and Engineering, vol. 671, 2020, https://doi.org/10.1088/1757-899X/671/1/012032.

[41] L. K. Fong, M. S. Islam, M. A. Ahmad, “Optimized PID Controller of DC-DC Buck Converter based on Archimedes Optimization Algorithm,” Journal of Robotics and Control (JRC), vol. 3, no. 4, pp. 658-672, 2023, https://doi.org/10.31763/ijrcs.v3i4.1113.

[42] G. M. Fadhil, I. A. Abed, and R. S. Jasim, “Genetic Algorithm Utilization to Fine Tune the Parameters of PID Controller,” Kufa Journal of Engineering, vol. 121, no. 2, pp. 1-12, 2021, http://dx.doi.org/10.30572/2018/KJE/120201.

[43] M. K. Oudah, M. Q. Sulttan, S. W. Shneen, “Fuzzy type 1 PID controllers design for TCP/AQM wireless networks,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 1, pp. 118-127, 2021, http://doi.org/10.11591/ijeecs.v21.i1.pp118-127.

[44] M. H. Jaber, M. K. Oudah, S. W. Shneen, “Genetic Simulation model of ANN and PID controller for TCP/AQM wireless networks by using MATLAB/Simulink,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 30, no. 2, pp. 739-747, 2023, http://doi.org/10.11591/ijeecs.v30.i2.pp739-747.

[45] A. Kundu, S. I. Tuhin, M. S. H, Sani, M. W. R. Easin, M. A. H. Masum, “Analytical Comparisons of the PID, ANN, and ANFIS Controllers' Performance in the AVR System,” International Journal of Innovative Science and Research Technology, vol. 8, no. 8, pp. 288-296, 2023, https://doi.org/10.5281/zenodo.8269112.

[46] I. Zaway, R. Jallouli-Khlif, B. Maalej, H. Medhaffar, and N. Derbel, “Multi-objective Fractional Order PID Controller Optimization for Kid’s Rehabilitation Exoskeleton,” International Journal of Robotics and Control Systems, vol. 3, no. 1, pp. 1-18, 2023, https://doi.org/10.31763/ijrcs.v3i1.840.

[47] J. Kong, J. Rodríguez, P. Hermosa, P. Huarancca, J Aréstegui and M. Chauca, “Characterization of Modified PID Controllers to Improve the Response Time of Angular Velocity to the Voltage Step in DC Motors by Means of a Mathematical Model Diagram Using SIMULINK Software,” Journal of Physics: Conference Series, vol. 2261, 2022, https://doi.org/10.1088/1742-6596/2261/1/012006.

[48] V. V. Kulkarni, V. A. Kulkarni, R. Talele, “PID Controller based DC Motor Speed Control,” International Journal on Recent and Innovation Trends in Computing and Communication, vol. 5, no. 9, pp. 35-38, 2017, https://doi.org/10.17762/ijritcc.v5i9.1208.

[49] Z. B Abdullah, H. S. Dakheel, and S. W. Shneen, “Simulation model of using ANN and PID controller for 2Ph-HSM by matlab,” AIP Conference Proceedings, vol. 3002, no. 1, p. 050004, 2024, https://doi.org/10.1063/5.0206748.

[50] R. Patra, P. Chaudhary, and O. A. Shah, “Design and Development of ANFIS based Controller for Three Phase Grid Connected System,” International Journal of Robotics and Control Systems, vol. 4, no. 1, pp. 125-138, 2024, https://doi.org/10.31763/ijrcs.v4i1.1242.

[51] M. H. Barhaghtalab, M. A. Sepestanaki, S. Mobayen, A. Jalilvand, A. Fekih, V. Meigoli, “Design of an adaptive fuzzy-neural inference system-based control approach for robotic manipulators,” Applied Soft Computing, vol. 149, p. 110970, 2024, https://doi.org/10.1016/j.asoc.2023.110970.

[52] Y. Al-Dunainawi, M. F. Abbod, and A. Jizany, “A New MIMO ANFIS-PSO Based NARMA-L2 Controller for Nonlinear Dynamic Systems,” Engineering Applications of Artificial Intelligence, vol. 62, pp. 265-275, 2017, https://doi.org/10.1016/j.engappai.2017.04.016.

[53] A. H. Issa, and A. H. Majeed, “Intelligent sensor fault detection based on soft computing,” Kufa Journal of Engineering, vol. 4, no. 1, pp. 113-124, 2012, https://doi.org/10.30572/2018/KJE/411246.

[54] D. Durano?lu, E. S. Alt?n, and ?. Küçük, “Optimization of adaptive neuro–fuzzy inference system (ANFIS) parameters via Box-Behnken experimental design approach: The prediction of chromium adsorption,” Heliyon, vol. 10, no. 3, p. e25813, 2024, https://doi.org/10.1016/j.heliyon.2024.e25813.

[55] H. Maghfiroh, C. Hermanu, and V. S. Z. Hizmi, “Position Control of VTOL System Using ANFIS Via Hardware in the Loop,” SINERGI, vol. 25, no. 3, pp. 309-318, 2021, http://doi.org/10.22441/sinergi.2021.3.008.

[56] S. M. Vatanchi, H. Etemadfard, M. F. Maghrebi, and R. Shad, “A Comparative Study on Forecasting of Long-term Daily Streamflow using ANN, ANFIS, BiLSTM, and CNN-GRU-LSTM,” Research Square, vol. 37, pp. 4769-4785, 2022, https://doi.org/10.21203/rs.3.rs-1443377/v1.

[57] E. Chukwuemeka, S. I. Mohammed, A. A. Umar, I. A. Abraham, and B. A. Ayobami, “Performance Evaluation of adaptive Neuro-Fuzzy Inference System for Modelling Dissolved Oxygen of Kubanni Reservoir: A case study in Zaria, Nigeria,” Environmental Health Engineering and Management Journal, vol. 9, no. 4, pp. 347-353, 2022, https://doi.org/10.34172/EHEM.2022.37.

[58] P. P. Kovac, D. Rodic, M. Gostimirovi?, B. Savkovi?, and D. Ješi?, “Adaptive Neuro-Fuzzy Modeling of Thermal Voltage Parameters for Tool Life Assessment in Face Milling,” Journal of Production Engineering, vol. 20, no. 1, pp. 16-20, 2017, https://doi.org/10.24867/JPE-2017-01-016.

[59] M. N. M. Salleh, N. Talpur, and K. Hussain, “Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions,” Data Mining and Big Data, pp. 527-535, 2017, https://doi.org/10.1007/978-3-319-61845-6_52.

[60] M. Alkashami, A. M. Taamneh, S. Khadragy, F. Shwedeh, A. Aburayya and S. A. Salloumd, “AI different approaches and ANFIS data mining: A novel approach to predicting early employment readiness in middle eastern nations,” International Journal of Data and Network Science, vol. 7, no. 3, pp. 1267–1282, 2023, https://doi.org/10.5267/j.ijdns.2023.4.011.

[61] S. I. Abbaa, S. J. Hadi, J. Abdullahi, “River water modelling prediction using multi-linear regression, artificial neural network, and adaptive neuro-fuzzy inference system techniques,” Procedia Computer Science, vol. 120, pp. 75-82, 2017, https://doi.org/10.1016/j.procs.2017.11.212.

[62] A. Abazari, M. Zadsar, M. Ghafouri, R. Atallah and C. Assi, “A Data Mining/ANFIS and Adaptive Control for Detection and Mitigation of Attacks on DC MGs,” IEEE Transactions on Smart Grid, vol. 14, no. 3, pp. 2406-2422, 2023, https://doi.org/10.1109/TSG.2022.3223279.


Refbacks

  • There are currently no refbacks.


Copyright (c) 2025 Manal Kadhim Oudah, Suad Ali Aessa, Salam Waley Shneen

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

About the JournalJournal PoliciesAuthor Information

International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia