Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO
Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beati...
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| Format: | Article |
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MDPI AG
2020-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/13/11/2914 |
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| author | Der-Fa Chen Yi-Cheng Shih Shih-Cheng Li Chin-Tung Chen Jung-Chu Ting |
| author_facet | Der-Fa Chen Yi-Cheng Shih Shih-Cheng Li Chin-Tung Chen Jung-Chu Ting |
| author_sort | Der-Fa Chen |
| collection | DOAJ |
| description | Because permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the motion of permanent-magnet SLM drive systems that enhance the robustness of the system. In order to reduce greater vibration in situations with uncertainty actions in the aforementioned control systems, we propose an adaptive modified recurrent Rogers–Szego polynomials neural network backstepping (AMRRSPNNB) control system with three adaptive laws and reimbursed controller with decorated gray wolf optimization (DGWO), in order to handle external bunched force uncertainty, including nonlinear friction, ending effects and time-varying dynamic uncertainties, as well as to reimburse the minimal rebuild error of the reckoned law. In accordance with the Lyapunov stability, online parameter training method of the modified recurrent Rogers–Szego polynomials neural network (MRRSPNN) can be derived by utilizing an adaptive law. Furthermore, to help reduce error and better obtain learning fulfillment, the DGWO algorithm was used to change the two learning rates in the weights of the MRRSPNN. Finally, the usefulness of the proposed control system is validated by tested results. |
| first_indexed | 2024-03-10T19:20:22Z |
| format | Article |
| id | doaj.art-e65d97af5c5a4cb7805e8860979ea18b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T19:20:22Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e65d97af5c5a4cb7805e8860979ea18b2023-11-20T03:02:34ZengMDPI AGEnergies1996-10732020-06-011311291410.3390/en13112914Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWODer-Fa Chen0Yi-Cheng Shih1Shih-Cheng Li2Chin-Tung Chen3Jung-Chu Ting4Department of Industrial Education and Technology, National Changhua University of Education, Changhua 500, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Changhua 500, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Changhua 500, TaiwanGraduate School of Vocational and Technological Education, National Yunlin University of Science and Technology, Yunlin 640, TaiwanDepartment of Industrial Education and Technology, National Changhua University of Education, Changhua 500, TaiwanBecause permanent-magnet synchronous linear motors (SLM) still exhibit nonlinear friction, ending effects and time-varying dynamic uncertainties, better control performances cannot be achieved by using common linear controllers. We propose a backstepping approach with three adaptive laws and a beating function to control the motion of permanent-magnet SLM drive systems that enhance the robustness of the system. In order to reduce greater vibration in situations with uncertainty actions in the aforementioned control systems, we propose an adaptive modified recurrent Rogers–Szego polynomials neural network backstepping (AMRRSPNNB) control system with three adaptive laws and reimbursed controller with decorated gray wolf optimization (DGWO), in order to handle external bunched force uncertainty, including nonlinear friction, ending effects and time-varying dynamic uncertainties, as well as to reimburse the minimal rebuild error of the reckoned law. In accordance with the Lyapunov stability, online parameter training method of the modified recurrent Rogers–Szego polynomials neural network (MRRSPNN) can be derived by utilizing an adaptive law. Furthermore, to help reduce error and better obtain learning fulfillment, the DGWO algorithm was used to change the two learning rates in the weights of the MRRSPNN. Finally, the usefulness of the proposed control system is validated by tested results.https://www.mdpi.com/1996-1073/13/11/2914Rogers–Szego polynomials neural networkgray wolf optimizationLyapunov stability theorembackstepping controlsynchronous linear motor |
| spellingShingle | Der-Fa Chen Yi-Cheng Shih Shih-Cheng Li Chin-Tung Chen Jung-Chu Ting Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO Energies Rogers–Szego polynomials neural network gray wolf optimization Lyapunov stability theorem backstepping control synchronous linear motor |
| title | Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO |
| title_full | Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO |
| title_fullStr | Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO |
| title_full_unstemmed | Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO |
| title_short | Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO |
| title_sort | permanent magnet slm drive system using amrrspnnb control system with dgwo |
| topic | Rogers–Szego polynomials neural network gray wolf optimization Lyapunov stability theorem backstepping control synchronous linear motor |
| url | https://www.mdpi.com/1996-1073/13/11/2914 |
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