Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations
The overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/1480732 |
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| author | Muhammad A. Shehu Ai-jun Li Bing Huang Yu Wang Bojian Liu |
| author_facet | Muhammad A. Shehu Ai-jun Li Bing Huang Yu Wang Bojian Liu |
| author_sort | Muhammad A. Shehu |
| collection | DOAJ |
| description | The overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model of the overhead crane was established and the sliding mode control (SMC) was proposed that ensured the existence of sliding motion in the presence of payload and hoisting height variations, and viscous frictions. To maximize the benefits derived from the proposed control method, novel sliding slope-update based on intelligent neural-network and fuzzy algorithms were developed to tune the controller, guaranteeing precise tracking of the actuated variables as well as regulation of the unactuated variables. The proposed methods adjust predetermined value of the sliding manifold’s slope in response to variations in hoisting heights. Control applications were conducted, and results based on graphical, integral absolute error (IAE), and integral time absolute error (ITAE) proved the effectiveness of the proposed algorithms. It was observed that the response of the controller with back-propagation-trained neural-network was more effective relative to that of the fuzzy algorithm. |
| format | Article |
| id | doaj-art-c41a76221a824b3695ddcfa59cf8b639 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-c41a76221a824b3695ddcfa59cf8b6392025-02-03T01:11:58ZengWileyJournal of Control Science and Engineering1687-52491687-52572019-01-01201910.1155/2019/14807321480732Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable VariationsMuhammad A. Shehu0Ai-jun Li1Bing Huang2Yu Wang3Bojian Liu4Control and Information Engineering, School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, ChinaControl and Information Engineering, School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, ChinaControl and Information Engineering, School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, ChinaControl and Information Engineering, School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, ChinaControl and Information Engineering, School of Automation, Northwestern Polytechnical University, Xi’an, Shaanxi 710072, ChinaThe overhead crane is required to operate fast and precisely with minimal sway. However, high-speed operations cause undesirable load sways, hazardous to operating personnel, the payload being handled, and the crane itself. Thus, a high-quality control is required. In this work, the nonlinear model of the overhead crane was established and the sliding mode control (SMC) was proposed that ensured the existence of sliding motion in the presence of payload and hoisting height variations, and viscous frictions. To maximize the benefits derived from the proposed control method, novel sliding slope-update based on intelligent neural-network and fuzzy algorithms were developed to tune the controller, guaranteeing precise tracking of the actuated variables as well as regulation of the unactuated variables. The proposed methods adjust predetermined value of the sliding manifold’s slope in response to variations in hoisting heights. Control applications were conducted, and results based on graphical, integral absolute error (IAE), and integral time absolute error (ITAE) proved the effectiveness of the proposed algorithms. It was observed that the response of the controller with back-propagation-trained neural-network was more effective relative to that of the fuzzy algorithm.http://dx.doi.org/10.1155/2019/1480732 |
| spellingShingle | Muhammad A. Shehu Ai-jun Li Bing Huang Yu Wang Bojian Liu Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations Journal of Control Science and Engineering |
| title | Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations |
| title_full | Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations |
| title_fullStr | Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations |
| title_full_unstemmed | Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations |
| title_short | Comparative Analysis of Neural-Network and Fuzzy Auto-Tuning Sliding Mode Controls for Overhead Cranes under Payload and Cable Variations |
| title_sort | comparative analysis of neural network and fuzzy auto tuning sliding mode controls for overhead cranes under payload and cable variations |
| url | http://dx.doi.org/10.1155/2019/1480732 |
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