Disturbance observer-based integral fuzzy sliding-mode control and its application to wind turbine system

• Published in: IET control theory & applications 2019-08, Vol.13 (12), p.1891-1900
• Main Authors: Hwang, Sounghwan, Park, Jin Bae, Joo, Young Hoon
• Format: Article
• Language: English
• Subjects: asymptotic stability; control system synthesis; disturbance observer‐based integral fuzzy sliding‐mode control methodology; external disturbance; fuzzy control; fuzzy controller membership function; fuzzy disturbance observer; imperfect premise matching condition; integral fuzzy sliding surface; linear matrix inequalities; Lyapunov methods; matched disturbance; nonlinear control systems; nonlinear permanent magnet synchronous generator; observers; Research Article; sliding motion; synchronous generators; system states; Takagi–Sugeno fuzzy model; variable structure systems; wind turbine system; wind turbines
• ISSN: 1751-8644; 1751-8652; 1751-8652
• DOI: 10.1049/iet-cta.2018.5779

In this study, a disturbance observer-based integral fuzzy sliding-mode control methodology for a wind turbine system with a non-linear permanent magnet synchronous generator (PMSG) is proposed using the Takagi–Sugeno fuzzy model. To reduce the hardware cost of the fuzzy controller membership function, an imperfect premise matching condition is considered. Considering the matched disturbance, and an external disturbance acting on the system, a novel integral fuzzy sliding surface is proposed based on a fuzzy disturbance observer (FDOB). Therefore, various forms of external disturbances can be estimated using the FDOB. In addition, a sufficient condition for guaranteeing the asymptotic stability of the sliding motion with $H_{\infty }$H∞ performance is proposed using a Lyapunov function and derived using a linear matrix inequality format. Furthermore, a reachability problem of the system states over the proposed sliding surface is derived through an appropriate Lyapunov function. Finally, the simulations for a wind turbine system using a PMSG are implemented to verify the effectiveness of the proposed method as compared with previous studies.