A novel double-level observer-based fault estimation for Takagi–Sugeno fuzzy systems with unknown nonlinear dynamics. (August 2019)

• Record Type: Journal Article
• Authors: Sun, Shaoxin; Zhang, Huaguang; Han, Jian; Liang, Yuling
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 41:Number 12(2019)
• Page Start: 3372
• Page End: 3384
• Publication Date: 2019-08
• Subjects: Takagi–Sugeno (T-S) fuzzy systems -- Kalman filter -- descriptor observer -- fault estimation -- generalized fuzzy hyperbolic model; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/0142331219826655
• Languages: English
• ISSNs: 0142-3312

Abstract

In this paper we investigate the fault estimation problem against local unknown nonlinear dynamics, sensor and actuator faults for a class of Takagi–Sugeno (T-S) fuzzy systems. In addition, the exogenous disturbances and measurement noise are considered, which are presented in the operation of the systems and are various and independent of the systems. A novel double-level observer is designed to estimate the system states and faults. Compared with the current research results, the proposed observer has a wider range of application. By designing a fuzzy augmented system and a Kalman filter as the first-level observer, the estimations of system states, sensor faults and actuator faults can be obtained simultaneously. The second-level observer can estimate the unknown nonlinear dynamic function by establishing generalized fuzzy hyperbolic model. The robust stability of the estimation error systems is considered by H ∞ performance. Finally, three simulation examples are provided to demonstrate the effectiveness of the proposed fault estimation method.