Performance Analysis of an Improved Particle Swarm Optimization and the Standard Particle Swarm Optimization
Patrick O. M. Ogutu1, Nicholas Oyie2, Winston Ojenge3
1Patrick O. M. Ogutu, Department of Electrical and Electronic Engineering, Murang’a University, Nairobi, Kenya, Kenya.
2Dr. Nicholas Oyie, Department of Electrical and Electronic Engineering, Murang’a University, Nairobi, Kenya, Kenya.
3Dr. Winston Ojenge, Department of Electrical and Electronic Engineering, Murang’a University, Nairobi, Kenya, Kenya.
Manuscript received on 06 September 2023 | Revised Manuscript received on 13 September 2023 | Manuscript Accepted on 15 October 2023 | Manuscript published on 30 October 2023 | PP: 37-42 | Volume-13 Issue-1, October 2023 | Retrieval Number: 100.1/ijeat.A42981013123 | DOI: 10.35940/ijeat.A4298.1013123
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: MATLAB Simulink, PID parameters, Iteration, nonlinear system, Particle swarm optimization Algorithm
Scope of the Article: Swarm Intelligence