Abstract
This paper is concerned with the fault estimation problem for a class of Takagi–Sugeno (T-S) fuzzy systems with actuator faults and sensor disturbances. Premise variables of the T-S fuzzy systems are assumed to be unmeasurable such that conventional parallel distributed compensation (PDC) methods are not applicable. A modified adaptive observer is designed to estimate states and fault parameters simultaneously. Finally, a simulation example is presented which shows the effectiveness of the proposed method.
Highlights
Due to a sudden disturbance or unnecessary changes, faults are always inevitable in actual systems
A lot of research has been done in fault diagnosis for both linear and nonlinear systems in the presence of event-triggered scheme, Markovian jump phenomena, and unknown membership functions etc
Fault-diagnosis schemes for T-S fuzzy model with unmeasurable premise variables were proposed based on a fuzzy PI observer and adaptive observer in [15] and [16], respectively, where faults are considered as unknown inputs in polynomials form
Summary
Due to a sudden disturbance or unnecessary changes, faults are always inevitable in actual systems. Fault-diagnosis schemes for T-S fuzzy model with unmeasurable premise variables were proposed based on a fuzzy PI observer and adaptive observer in [15] and [16], respectively, where faults are considered as unknown inputs in polynomials form. For discrete-time T-S fuzzy system influenced by sensor faults and unknown disturbances, an H–/H∞ robust fault-detection observer was proposed in [20] by using descriptor approach and nonquadratic Lyapunov functions, whereas the T-S fuzzy systems with unmeasurable premise variables were considered in [21].
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