LAPSE:2023.13247v1
Published Article

LAPSE:2023.13247v1
Modeling and Fuzzy FOPID Controller Tuned by PSO for Pneumatic Positioning System
March 1, 2023
Abstract
A pneumatic cylinder system is believed to be extremely nonlinear and sensitive to nonlinearities, which makes it challenging to establish precise position control of the actuator. The current research is aimed at reducing the overshoot in the response of a double-acting pneumatic actuator, namely, the IPA positioning system’s reaction time. The pneumatic system was modeled using an autoregressive with exogenous input (ARX) model structure, and the control strategy was implemented using a fuzzy fractional order proportional integral derivative (fuzzy FOPID) employing the particle swarm optimization (PSO) algorithm. This approach was used to determine the optimal controller parameters. A comparison study has been conducted to prove the advantages of utilizing a PSO fuzzy FOPID controller over PSO fuzzy PID. The controller tuning algorithm was validated and tested using a pneumatic actuator system in both simulation and real environments. From the standpoint of time-domain performance metrics, such as rising time (tr), settling time (ts), and overshoot (OS%), the PSO fuzzy FOPID controller outperforms the PSO Fuzzy PID controller in terms of dynamic performance.
A pneumatic cylinder system is believed to be extremely nonlinear and sensitive to nonlinearities, which makes it challenging to establish precise position control of the actuator. The current research is aimed at reducing the overshoot in the response of a double-acting pneumatic actuator, namely, the IPA positioning system’s reaction time. The pneumatic system was modeled using an autoregressive with exogenous input (ARX) model structure, and the control strategy was implemented using a fuzzy fractional order proportional integral derivative (fuzzy FOPID) employing the particle swarm optimization (PSO) algorithm. This approach was used to determine the optimal controller parameters. A comparison study has been conducted to prove the advantages of utilizing a PSO fuzzy FOPID controller over PSO fuzzy PID. The controller tuning algorithm was validated and tested using a pneumatic actuator system in both simulation and real environments. From the standpoint of time-domain performance metrics, such as rising time (tr), settling time (ts), and overshoot (OS%), the PSO fuzzy FOPID controller outperforms the PSO Fuzzy PID controller in terms of dynamic performance.
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Keywords
fuzzy FOPID, fuzzy PID, intelligent pneumatic actuators, PSO algorithm
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Suggested Citation
Muftah MN, Faudzi AAM, Sahlan S, Shouran M. Modeling and Fuzzy FOPID Controller Tuned by PSO for Pneumatic Positioning System. (2023). LAPSE:2023.13247v1
Author Affiliations
Muftah MN: Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; Department of Control Engineering, College of Electronics Technology, Bani Walid P.O. Box 38645, Libya [ORCID]
Faudzi AAM: Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Jalan Sultan Yahya P [ORCID]
Sahlan S: Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Jalan Sultan Yahya P
Shouran M: Department of Control Engineering, College of Electronics Technology, Bani Walid P.O. Box 38645, Libya; Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK [ORCID]
Faudzi AAM: Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Jalan Sultan Yahya P [ORCID]
Sahlan S: Department of Control and Mechatronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; Centre for Artificial Intelligence and Robotics (CAIRO), Universiti Teknologi Malaysia, Jalan Sultan Yahya P
Shouran M: Department of Control Engineering, College of Electronics Technology, Bani Walid P.O. Box 38645, Libya; Wolfson Centre for Magnetics, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK [ORCID]
Journal Name
Energies
Volume
15
Issue
10
First Page
3757
Year
2022
Publication Date
2022-05-19
ISSN
1996-1073
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PII: en15103757, Publication Type: Journal Article
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LAPSE:2023.13247v1
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https://doi.org/10.3390/en15103757
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Mar 1, 2023
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