Fuzzy fractional-order adaptive robust feedback linearization control optimized by the multi-objective artificial hummingbird algorithm for a nonlinear ball–wheel system
For stabilizing a nonlinear ball–wheel system, this research suggests a fuzzy fractional-order adaptive robust feedback linearization control approach. The feedback linearization-based controller uses the decoupled sliding mode to locate the sliding surfaces and determine the adaptive coefficients....
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| Published in: | Journal of the Brazilian Society of Mechanical Sciences and Engineering Vol. 45; no. 11; p. 575 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.11.2023
Springer Nature B.V |
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| ISSN: | 1678-5878, 1806-3691 |
| Online Access: | Get full text |
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| Abstract | For stabilizing a nonlinear ball–wheel system, this research suggests a fuzzy fractional-order adaptive robust feedback linearization control approach. The feedback linearization-based controller uses the decoupled sliding mode to locate the sliding surfaces and determine the adaptive coefficients. Then, the fractional-order calculus and fuzzy logic-based systems are implemented to improve the efficiency of the controller. Additionally, the multi-objective artificial hummingbird algorithm (MAHA) is employed to optimize the control coefficients by including two objective functions, namely the integral of the absolute values of the control efforts and the system errors. The success of the suggested strategy is then evaluated by applying it on the ball–wheel system and comparing the outcomes to those documented in the literature. |
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| AbstractList | For stabilizing a nonlinear ball–wheel system, this research suggests a fuzzy fractional-order adaptive robust feedback linearization control approach. The feedback linearization-based controller uses the decoupled sliding mode to locate the sliding surfaces and determine the adaptive coefficients. Then, the fractional-order calculus and fuzzy logic-based systems are implemented to improve the efficiency of the controller. Additionally, the multi-objective artificial hummingbird algorithm (MAHA) is employed to optimize the control coefficients by including two objective functions, namely the integral of the absolute values of the control efforts and the system errors. The success of the suggested strategy is then evaluated by applying it on the ball–wheel system and comparing the outcomes to those documented in the literature. |
| ArticleNumber | 575 |
| Author | Mazinan, A. H. Mahmoodabadi, M. J. Nekoui, M. A. Arbatsofla, S. Moghtader |
| Author_xml | – sequence: 1 givenname: S. Moghtader surname: Arbatsofla fullname: Arbatsofla, S. Moghtader organization: Department of Control Engineering, Faculty of Electrical Engineering, South Tehran Branch, Islamic Azad University – sequence: 2 givenname: A. H. surname: Mazinan fullname: Mazinan, A. H. email: ahmazinan@gmail.com organization: Department of Control Engineering, Faculty of Electrical Engineering, South Tehran Branch, Islamic Azad University – sequence: 3 givenname: M. J. surname: Mahmoodabadi fullname: Mahmoodabadi, M. J. organization: Department of Mechanical Engineering, Sirjan University of Technology – sequence: 4 givenname: M. A. surname: Nekoui fullname: Nekoui, M. A. organization: Department of Control Engineering, Faculty of Electrical Engineering, South Tehran Branch, Islamic Azad University |
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| Keywords | Robust control Fuzzy system Multi-objective artificial hummingbird algorithm Optimal control Feedback linearization Adaptive control Fractional calculus |
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| SubjectTerms | Algorithms Archives & records Calculus Control systems Control theory Controllers Design engineering Dynamical systems Engineering Feedback linearization Fuzzy logic Genetic algorithms Kinematics Mechanical Engineering Optimization Parameter estimation Pareto optimum Performance evaluation Robust control Sliding mode control Systems stability Technical Paper |
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| Title | Fuzzy fractional-order adaptive robust feedback linearization control optimized by the multi-objective artificial hummingbird algorithm for a nonlinear ball–wheel system |
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