Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis
This research seeks to promote the field via the design and implementation optimized robotic manipulator control systems, recognizing control techniques' vital role in current engineering applications. This study introduces an improved particle swarm optimization (IPSO) technique that maximizes...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024013446 |
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| author | Gamil Ahmed Ahmed Eltayeb Nezar M. Alyazidi Imil Hamda Imran Tarek Sheltami Sami El-Ferik |
| author_facet | Gamil Ahmed Ahmed Eltayeb Nezar M. Alyazidi Imil Hamda Imran Tarek Sheltami Sami El-Ferik |
| author_sort | Gamil Ahmed |
| collection | DOAJ |
| description | This research seeks to promote the field via the design and implementation optimized robotic manipulator control systems, recognizing control techniques' vital role in current engineering applications. This study introduces an improved particle swarm optimization (IPSO) technique that maximizes the efficiency of a Fractional Order Proportional-Integral-Derivative (FOPID) controller by optimally adjusting FOPID gains in robotic manipulator systems. The controller has undergone refinement and enhancement using state-of-the-art particle swarm optimization (PSO) techniques incorporating a cost function and a representative bio-inspired algorithm. The IPSO algorithm enhances global search efficiency by preventing premature convergence and local minima trapping through chaos-based initialization and adaptive mutation strategies. The performance of IPSO-tuned FOPID controllers is benchmarked against conventional PSO-tuned FOPID controllers using various objective functions. The stabilizing fractional order PID controllers demonstrated a higher stability margin than traditional PID controllers. Numerical simulations support the developed strategy by analyzing the step and sinusoidal responses of the closed-loop system within the stability region. The results indicate that IPSO outperforms PSO with improvements of approximately 50% for 10 iterations, about 12% for 50 iterations, and around 20% for 100 iterations across ITSE, ITAE, and ITAE metrics, respectively. Furthermore the statistical analysis based on Wilcoxon sign rank test proof that the IPSO algorithm significantly improves convergence speed, controller accuracy, and overall performance, thereby enhancing the effectiveness of the IPSO technique such as in case of 10 iteration the confidence intervals do not include zeros, which indicates that IPSO outperformed the traditional POS in all scenarios. |
| format | Article |
| id | doaj-art-7e4885158bdc4f3f909cf52c7f145cd2 |
| institution | DOAJ |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-7e4885158bdc4f3f909cf52c7f145cd22025-08-20T02:52:24ZengElsevierResults in Engineering2590-12302024-12-012410308910.1016/j.rineng.2024.103089Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysisGamil Ahmed0Ahmed Eltayeb1Nezar M. Alyazidi2Imil Hamda Imran3Tarek Sheltami4Sami El-Ferik5Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi ArabiaInterdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi ArabiaInterdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi ArabiaApplied Research Center for Metrology, Standards and Testing, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi ArabiaInterdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; Computer Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi ArabiaInterdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia; Control and Instrumentation Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi ArabiaThis research seeks to promote the field via the design and implementation optimized robotic manipulator control systems, recognizing control techniques' vital role in current engineering applications. This study introduces an improved particle swarm optimization (IPSO) technique that maximizes the efficiency of a Fractional Order Proportional-Integral-Derivative (FOPID) controller by optimally adjusting FOPID gains in robotic manipulator systems. The controller has undergone refinement and enhancement using state-of-the-art particle swarm optimization (PSO) techniques incorporating a cost function and a representative bio-inspired algorithm. The IPSO algorithm enhances global search efficiency by preventing premature convergence and local minima trapping through chaos-based initialization and adaptive mutation strategies. The performance of IPSO-tuned FOPID controllers is benchmarked against conventional PSO-tuned FOPID controllers using various objective functions. The stabilizing fractional order PID controllers demonstrated a higher stability margin than traditional PID controllers. Numerical simulations support the developed strategy by analyzing the step and sinusoidal responses of the closed-loop system within the stability region. The results indicate that IPSO outperforms PSO with improvements of approximately 50% for 10 iterations, about 12% for 50 iterations, and around 20% for 100 iterations across ITSE, ITAE, and ITAE metrics, respectively. Furthermore the statistical analysis based on Wilcoxon sign rank test proof that the IPSO algorithm significantly improves convergence speed, controller accuracy, and overall performance, thereby enhancing the effectiveness of the IPSO technique such as in case of 10 iteration the confidence intervals do not include zeros, which indicates that IPSO outperformed the traditional POS in all scenarios.http://www.sciencedirect.com/science/article/pii/S2590123024013446Particle swarm optimizationFractional order PIDRobot manipulatorImproved PSOGain tuningOptimization |
| spellingShingle | Gamil Ahmed Ahmed Eltayeb Nezar M. Alyazidi Imil Hamda Imran Tarek Sheltami Sami El-Ferik Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis Results in Engineering Particle swarm optimization Fractional order PID Robot manipulator Improved PSO Gain tuning Optimization |
| title | Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis |
| title_full | Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis |
| title_fullStr | Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis |
| title_full_unstemmed | Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis |
| title_short | Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis |
| title_sort | improved particle swarm optimization for fractional order pid control design in robotic manipulator system a performance analysis |
| topic | Particle swarm optimization Fractional order PID Robot manipulator Improved PSO Gain tuning Optimization |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024013446 |
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