Performance Analysis of an Improved Particle Swarm Optimization and the Standard Particle Swarm Optimization

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Patrick O. M. Ogutu
Dr. Nicholas Oyie
Dr. Winston Ojenge

Abstract

Many industries employ different modes of control when it comes to PID parameter tuning. The problem of tuning a control system for linear and nonlinear systems has been undertaken by previous authors however the level of error reduction in the system performance has not been done quite well, hence the study on improved particle swarm optimization using improved Algorithm for PID parameter tuning. This paper tackled optimization of PID parameters based on improved PSO algorithm for the non-linear system. The particle swarm optimization is used to tune the PID parameters to ensure improved system response and operation. The PSO was deployed in a nonlinear system for application and validation of results achieved through PID tuning of the standard parameters on the MATLAB Simulink platform. The study ensured that the PID parameters were effectively tuned by applying improved PSO Algorithm to the plant process. The research used a standard nonlinear system depicting the real-life situation and an Improved Particle Swarm Optimization Algorithm to analyze and compare the improved behavior on the MATLAB/Simulink toolbox as applied to the PID parameters. Finally, it was logically realized that an improved PSO Algorithm system response was much better in comparison with the non-PSO tuned system. The simulation was performed on the plant transfer function using the MATLAB and Simulink platforms at various parameter choices and situations, and realizations were made from the data obtained. As the iteration was increased from 10, 50, and 100, there was a significant reduction in ITAE error from 0.054806 to a minimum of 0.01900, which is far better than the SPSO algorithm. SPSO reduces the error from 0.065143 to 0.020476. It was noted that the system behavior was far better in terms of settling time and peak overshoot for IPSO.

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How to Cite
[1]
Patrick O. M. Ogutu, Dr. Nicholas Oyie, and Dr. Winston Ojenge , Trans., “Performance Analysis of an Improved Particle Swarm Optimization and the Standard Particle Swarm Optimization”, IJEAT, vol. 13, no. 1, pp. 37–42, Nov. 2023, doi: 10.35940/ijeat.A4298.1013123.
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Author Biographies

Patrick O. M. Ogutu, Department of Electrical and Electronic Engineering, Murang'a University, Nairobi, Kenya, Kenya.

Mr. Patrick O.M. Ogutu, An experienced and dedicated instrumentation and control technologist with over 23 years hands-on experience in training, and mentorship highly skilled and competent Instrumentation and control technicians who have excelled in their workplaces as revealed by the industrial liaison office. My short-term goal is to obtain Mtech in control and instrumentation and the next five years obtain Ph.D. in instrumentation astronomy specializing in the design, maintenance, and calibration of modern astronomy instruments after successfully undergoing the basic DARA-sponsored training where I gained insight and interest. I am currently in charge of the control and automation laboratory, a reliable and honest technologist who has excelled in the field of automation, performing duties with less supervision, timely handling of work and strictly adhering to deadlines. Master of Technology (Instrumentation and Control) Murang’a University of Science and Technology (student) Bphil Technology (Automation and control) TUK. Higher Diploma in Electrical and Electronic Engineering, Mombasa Polytechnic Collage, Diploma in Technology (Instrumentation and control) ,Mombasa Polytechnic Collage ,A-Level Certificate, K.A.C.E. ,Shimo-la Tewa High school-Level Certificate, K.C.E. Alidina Visram High school. 

Dr. Nicholas Oyie, Department of Electrical and Electronic Engineering, Murang'a University, Nairobi, Kenya, Kenya.

Dr. Nicholas Oyie, (Ph.D.), -Lecturer and Chairman of the Department EEE School of Engineering and Technology, Murang'a University of Technology, Kenya. PhD. in Electronic Engineering from the University of KwaZulu-Natal, South Africa. Registered Graduate Engineer with the Engineers Board of Kenya (EBK). Measurements and analysis of large-scale path loss model at 14 and 22 GHz in indoor corridor, Performance study of path loss models at 14, 18, and 22 GHz in an indoor corridor environment for wireless communications, An author of the following: A comparative study of dual-slope path loss model in various indoor environments at 14 to 22 GHz,Investigating the impact of antenna heights on path loss models in an indoor corridor environment, An empirical approach to omnidirectional path loss and line-of-sight probability models at 18 GHz for 5G networks, Propagation path loss prediction modelling in enclosed environments for 5G networks: Contact

Dr. Winston Ojenge, Department of Electrical and Electronic Engineering, Murang'a University, Nairobi, Kenya, Kenya.

Dr. Winston Ochieng Ojenge, (PhD), Computer science at the T-UK, (M.Sc.) Information Systems UON (Kenya) 2008, (B.Ed.) technology Moi University (Kenya).Lecturer, Control and Automation Engineering. He heads the Digital Economy Program. He has a PhD in Computer Science from Technical University of Kenya. His research interests are artificial intelligence and machine learning, and IoT. He was the founder coordinator of the Innovation Lab at the Technical University of Kenya, and holds 3 patents in the areas of telecommunications and AI, with 3 other patent applications under review at Kenya Institute of Intellectual Property. He is currently the Co-Lead for the AI4D PhD Scholarships in AI.The Contact email is

How to Cite

[1]
Patrick O. M. Ogutu, Dr. Nicholas Oyie, and Dr. Winston Ojenge , Trans., “Performance Analysis of an Improved Particle Swarm Optimization and the Standard Particle Swarm Optimization”, IJEAT, vol. 13, no. 1, pp. 37–42, Nov. 2023, doi: 10.35940/ijeat.A4298.1013123.
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References

E. Y. Bejarbaneh and A. Bagheri, "A new adjustment technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm," 2019. https://doi.org/10.1016/j.asoc.2019.105822

V. Fevrier and a. et, "Fuzzy dynamic parameter adaptation in ACO and PSO for designing fuzzy controller –the case of water level and temperature control," 2018. https://doi.org/10.1155/2018/1274969

J. Dombi and A. Hissein, "A new approach to fuzzy control using the distancing function," Journal of Process Control, 2020. https://doi.org/10.1016/j.jprocont.2019.12.005

S. K. Rath and B. K. Pangrahi, "Performance comparison of various Evolutionary Algorithm for Oprional Reactive Power dispatch," 2013.

P. A. Hokam and et al, Lecture notes on nonlinear systems and control,Switzerland : ABB Ltd, 2018.

J. Iqbal and et al, "Non Linear control system," vol. 6, no. 4, pp. 301-312, 2017.

M. Maldini, "PID control Explained," https://maldus512.medium.com/pid-control-explained-45b671f10bc, 2018.

O. Wu and J. Zeng, "A modified particle swarm optimization algorithm for economic dispatch problem," 2015.

M.W.Berry and R.K.Wood, "Chemical Engineering science," Pergamon Press, vol. 28, pp. 1707-1712, 1973. https://doi.org/10.1016/0009-2509(73)80025-9

S. Chakraborty and U. Maulik, "Design of Symmetrical FIR Digital filters using PSO," 2007.

Krishna, V. S. S., Misra, Y., & Rao, G. A. (2019). FPGA Implementation of Variable Feed Rate Algorithm for a Three Input Fuzzy Controller to Maintain the Cane Level. In International Journal of Innovative Technology and Exploring Engineering (Vol. 8, Issue 10, pp. 3900–3915). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijitee.j9908.0881019

Singh, N., Chawla, M. P. S., & Bhongade, S. (2022). Home Energy Management System for Dynamic Loads using Mamdani Fuzzy Logic Approach. In International Journal of Emerging Science and Engineering (Vol. 10, Issue 3, pp. 1–13). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijese.c2521.0110322

Sharma, P. (2023). A Fuzzy Approach to Educational Grading Systems “Fuzzy Logic Based Grade Card.” In International Journal of Advanced Engineering and Nano Technology (Vol. 10, Issue 6, pp. 1–8). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijaent.g9582.0610623

R.*, A. K. K., & Dhas, Dr. E. R. (2020). Performance Analysis of Opposition Based Particle Swarm Optimization with Cauchy Distribution in Minimizing Makespan Time in Job Shop Scheduling. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 360–366). Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP. https://doi.org/10.35940/ijrte.d8524.038620

Ahmed, M. M. (2021). SALP Swarm Optimization Approach for Maximization The Lifetime of Wireless Sensor Network. In Indian Journal of Data Communication and Networking (Vol. 1, Issue 2, pp. 16–20). Lattice Science Publication (LSP). https://doi.org/10.54105/ijdcn.b5006.041221

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