Hysteresis Modeling of a PAM System Using ANFIS
Pneumatic artificial muscles (PAMs) are excellent environmentally friendly actuators and springs that remain somewhat underutilized in the industry due to their hysteretic behavior, which makes predicting their behavior difficult. This paper presents a novel black-box approach that employs an adapti...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Actuators |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-0825/10/11/280 |
| _version_ | 1827677817359302656 |
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| author | Saad Abu Mohareb Adham Alsharkawi Moudar Zgoul |
| author_facet | Saad Abu Mohareb Adham Alsharkawi Moudar Zgoul |
| author_sort | Saad Abu Mohareb |
| collection | DOAJ |
| description | Pneumatic artificial muscles (PAMs) are excellent environmentally friendly actuators and springs that remain somewhat underutilized in the industry due to their hysteretic behavior, which makes predicting their behavior difficult. This paper presents a novel black-box approach that employs an adaptive-network-based fuzzy inference system (ANFIS) to create pressure-contraction hysteresis models. The resulting models are simulated in a control system toolbox to test their controllability using a simple proportional-integral (PI) controller. The data showed that the models created based on fixed inputs had an average normalized root mean square error (RMSE) of 0.0327, and their generalized counterparts achieved an average normalized RMSE of 0.04087. The simulation results showed that the PI controller was able to achieve mean tracking errors of 8.1 µm and 18.3 µm when attempting to track a sinusoidal and step references, respectively. This work concludes that modeling using the ANFIS is limited to being able to know the derivative of the input pressure or its rate of change, but competently models hysteresis in PAMs across multiple operating ranges. This is the highlight of this work. Additionally, these ANFIS-created models lend themselves well to controller, but exploring more refined control schemes is necessary to fully utilize them. |
| first_indexed | 2024-03-10T05:48:12Z |
| format | Article |
| id | doaj.art-8eec13b5d0d84f38861e5c29baf87e3f |
| institution | Directory Open Access Journal |
| issn | 2076-0825 |
| language | English |
| last_indexed | 2024-03-10T05:48:12Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Actuators |
| spelling | doaj.art-8eec13b5d0d84f38861e5c29baf87e3f2023-11-22T21:56:53ZengMDPI AGActuators2076-08252021-10-01101128010.3390/act10110280Hysteresis Modeling of a PAM System Using ANFISSaad Abu Mohareb0Adham Alsharkawi1Moudar Zgoul2Department of Mechatronics Engineering, University of Jordan, Amman 11942, JordanDepartment of Mechatronics Engineering, University of Jordan, Amman 11942, JordanDepartment of Mechatronics Engineering, University of Jordan, Amman 11942, JordanPneumatic artificial muscles (PAMs) are excellent environmentally friendly actuators and springs that remain somewhat underutilized in the industry due to their hysteretic behavior, which makes predicting their behavior difficult. This paper presents a novel black-box approach that employs an adaptive-network-based fuzzy inference system (ANFIS) to create pressure-contraction hysteresis models. The resulting models are simulated in a control system toolbox to test their controllability using a simple proportional-integral (PI) controller. The data showed that the models created based on fixed inputs had an average normalized root mean square error (RMSE) of 0.0327, and their generalized counterparts achieved an average normalized RMSE of 0.04087. The simulation results showed that the PI controller was able to achieve mean tracking errors of 8.1 µm and 18.3 µm when attempting to track a sinusoidal and step references, respectively. This work concludes that modeling using the ANFIS is limited to being able to know the derivative of the input pressure or its rate of change, but competently models hysteresis in PAMs across multiple operating ranges. This is the highlight of this work. Additionally, these ANFIS-created models lend themselves well to controller, but exploring more refined control schemes is necessary to fully utilize them.https://www.mdpi.com/2076-0825/10/11/280PAMsANFIShysteresismodelingcontrolFESTO |
| spellingShingle | Saad Abu Mohareb Adham Alsharkawi Moudar Zgoul Hysteresis Modeling of a PAM System Using ANFIS Actuators PAMs ANFIS hysteresis modeling control FESTO |
| title | Hysteresis Modeling of a PAM System Using ANFIS |
| title_full | Hysteresis Modeling of a PAM System Using ANFIS |
| title_fullStr | Hysteresis Modeling of a PAM System Using ANFIS |
| title_full_unstemmed | Hysteresis Modeling of a PAM System Using ANFIS |
| title_short | Hysteresis Modeling of a PAM System Using ANFIS |
| title_sort | hysteresis modeling of a pam system using anfis |
| topic | PAMs ANFIS hysteresis modeling control FESTO |
| url | https://www.mdpi.com/2076-0825/10/11/280 |
| work_keys_str_mv | AT saadabumohareb hysteresismodelingofapamsystemusinganfis AT adhamalsharkawi hysteresismodelingofapamsystemusinganfis AT moudarzgoul hysteresismodelingofapamsystemusinganfis |