Fault reconstruction for Takagi–Sugeno fuzzy systems via learning observers

ABSTRACT

This paper addresses the problem of observer-based fault reconstruction for Takagi–Sugeno fuzzy systems. Two types of fuzzy learning observers are constructed to achieve simultaneous reconstruction of system states and actuator faults. Stability and convergence of the proposed observers are proved using Lyapunov stability theory, and necessary conditions for the existence of the observers are further discussed. The design of fuzzy learning observers can be formulated in terms of a series of linear matrix inequalities that can be conveniently solved using convex optimisation technique. A single-link flexible manipulator is employed to verify the effectiveness of the proposed fault-reconstructing approaches.

Keywords:

• Fault reconstruction
• Takagi–Sugeno fuzzy systems
• observer design
• learning observers
• linear matrix inequalities

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61203185 and No. 61304237).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was partially supported by the National Natural Science Foundation of China [grant number 61203185], [grant number 61304237].