A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective
Sliding mode control is a promising approach for designing controllers for systems with empirical characteristics. This is a favored nonlinear control strategy that effectively addresses the uncertainties present in derived mathematical models. To further enhance the stability of such systems, an Ad...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10379157/ |
| _version_ | 1797361488878370816 |
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| author | Jim George Geetha Mani |
| author_facet | Jim George Geetha Mani |
| author_sort | Jim George |
| collection | DOAJ |
| description | Sliding mode control is a promising approach for designing controllers for systems with empirical characteristics. This is a favored nonlinear control strategy that effectively addresses the uncertainties present in derived mathematical models. To further enhance the stability of such systems, an Adaptive Neuro Fuzzy Inference System is employed by adapting to dynamic changes and inconsistent correlations between excitation and response. In this study, Sliding Mode Control was deployed in the feedback loop, effectively serving as a state feedback controller based on a nonlinear control law. As a two-parameter control approach, Sliding Mode Control requires careful tuning to achieve optimal performance. The integration of the Adaptive Neuro-Fuzzy System aims to bestow the two parameters of Sliding Mode Control with the ability to rapidly reduce errors to zero, thereby enhancing overall control efficiency. The research focuses on utilizing an Adaptive Neuro Fuzzy Inference System to implement Sliding Mode Control for a DC servo system while emphasizing state feedback control. The Harmony Search Optimization method is employed to optimize controller parameters effectively. The results of the research demonstrate the achievement of a best-fit value, where the minimal standard error and Best fitness are considered. This highlights the successful integration of the proposed control strategy and validates its effectiveness in providing accurate and reliable control of the real-time DC servo system. |
| first_indexed | 2024-03-08T15:54:24Z |
| format | Article |
| id | doaj.art-29a4c02589654bf186d0a5627b77e28a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T15:54:24Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-29a4c02589654bf186d0a5627b77e28a2024-01-09T00:04:10ZengIEEEIEEE Access2169-35362024-01-01123222323910.1109/ACCESS.2023.334883610379157A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization PerspectiveJim George0Geetha Mani1https://orcid.org/0000-0002-8234-9294School of Electrical Engineering (SELECT), Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Electrical Engineering (SELECT), Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSliding mode control is a promising approach for designing controllers for systems with empirical characteristics. This is a favored nonlinear control strategy that effectively addresses the uncertainties present in derived mathematical models. To further enhance the stability of such systems, an Adaptive Neuro Fuzzy Inference System is employed by adapting to dynamic changes and inconsistent correlations between excitation and response. In this study, Sliding Mode Control was deployed in the feedback loop, effectively serving as a state feedback controller based on a nonlinear control law. As a two-parameter control approach, Sliding Mode Control requires careful tuning to achieve optimal performance. The integration of the Adaptive Neuro-Fuzzy System aims to bestow the two parameters of Sliding Mode Control with the ability to rapidly reduce errors to zero, thereby enhancing overall control efficiency. The research focuses on utilizing an Adaptive Neuro Fuzzy Inference System to implement Sliding Mode Control for a DC servo system while emphasizing state feedback control. The Harmony Search Optimization method is employed to optimize controller parameters effectively. The results of the research demonstrate the achievement of a best-fit value, where the minimal standard error and Best fitness are considered. This highlights the successful integration of the proposed control strategy and validates its effectiveness in providing accurate and reliable control of the real-time DC servo system.https://ieeexplore.ieee.org/document/10379157/Adaptive neuro fuzzy inference system (ANFIS)best fitnessDC servoHarmony search algorithmsliding mode control (SMC)standard error |
| spellingShingle | Jim George Geetha Mani A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective IEEE Access Adaptive neuro fuzzy inference system (ANFIS) best fitness DC servo Harmony search algorithm sliding mode control (SMC) standard error |
| title | A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective |
| title_full | A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective |
| title_fullStr | A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective |
| title_full_unstemmed | A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective |
| title_short | A Portrayal of Sliding Mode Control Through Adaptive Neuro Fuzzy Inference System With Optimization Perspective |
| title_sort | portrayal of sliding mode control through adaptive neuro fuzzy inference system with optimization perspective |
| topic | Adaptive neuro fuzzy inference system (ANFIS) best fitness DC servo Harmony search algorithm sliding mode control (SMC) standard error |
| url | https://ieeexplore.ieee.org/document/10379157/ |
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