A Type-2 Fuzzy Controller for Floating Tension-Leg Platforms in Wind Turbines

Behnam Firouzi, Khalid A. Alattas, Mohsen Bakouri, Abdullah K. Alanazi, Ardashir Mohammadzadeh, Saleh Mobayen and Afef Fekih
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Behnam Firouzi: Vibrations and Acoustics Laboratory (VAL), Mechanical Engineering Department, Ozyegin University, Istanbul 34794, Turkey
Khalid A. Alattas: Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
Mohsen Bakouri: Department of Medical Equipment Technology, College of Applied Medical Science, Majmaah University, Majmaah 11952, Saudi Arabia
Abdullah K. Alanazi: Department of Chemistry, Faculty of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
Ardashir Mohammadzadeh: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Saleh Mobayen: Future Technology Research Center, National Yunlin University of Science and Technology, Douliu 64002, Taiwan
Afef Fekih: Department of Electrical and Computer Engineering, University of Louisiana at Lafayette, Lafayette, LA 70504, USA

Energies, 2022, vol. 15, issue 5, 1-19

Abstract: This paper proposes a type-2 fuzzy controller for floating tension-leg platforms in wind turbines. Its main objective is to stabilize and control offshore floating wind turbines exposed to oscillating motions. The proposed approach assumes that the dynamics of all units are completely unknown. The latter are approximated using the proposed Sugeno-based type-2 fuzzy approach. A nonlinear Kalman-based algorithm is developed for parameter optimization, and linear matrix inequalities are derived to analyze the system’s stability. For the fuzzy system, both rules and membership functions are optimized. Additionally, in the designed approach, the estimation error of the type-2 fuzzy approach is also considered in the stability analysis. The effectiveness and performance of the proposed approach is assessed using a simulation study of a tension leg platform subject to various disturbance modes.

Keywords: wind turbine; intelligent control; type-2 fuzzy control (T2FLC); learning algorithm; tension leg platforms (TLP) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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