Lower triangle factor based sliding mode observers design for T-S fuzzy systems with faults.

The paper mainly focuses on the fault estimation for a class of Takagi-Sugeno (T-S) fuzzy systems with faults. A synthetic estimation observer design method is proposed. The synthetic estimation observer can cover the robust observer, adaptive observer and intermediate estimation observer in the existing study work. Based on the observer design method, an LTF-based sliding mode observer (SMO) is designed for the T-S fuzzy system in consideration. Under the observer, the fault occurring in the system can be well estimated. The obtained LMI-based conditions guarantee the states of the error dynamics to be uniformly ultimately bounded. A numerical example tests the proposed method.

Convergent Estimation Mechanism Design for Nonlinear Fuzzy Systems With Faults.

The convergent estimation for a class of nonlinear Takagi-Sugeno fuzzy systems is concerned, where time-varying process faults and input disturbances are both involved. A convergent estimation mechanism (CEM) based on a set of fuzzy iterative estimation observers is constructed for the nonlinear fuzzy system; meanwhile, the convergence of the mean sequence of estimation errors (for both states and faults) to zero (vector) is proved. However, in the existing literature, the estimation errors can only be proved to be uniformly ultimately bounded when the fault is time varying. In the design procedure, the disturbances on systems in consideration can be isolated effectively in the obtained fuzzy iterative error dynamics through introducing a suitable isolation technique. Numerical examples give the simulation results to show the effectiveness and merits of the proposed CEM.

Fault Estimation for Fuzzy Delay Systems: A Minimum Norm Least Squares Solution Approach.

This paper mainly focuses on the problem of fault estimation for a class of Takagi-Sugeno fuzzy systems with state delays. A minimum norm least squares solution (MNLSS) approach is first introduced to establish a fault estimation compensator, which is able to optimize the fault estimator. Compared with most of the existing fault estimation methods, the MNLSS-based fault estimation method can effectively decrease the effect of state errors on the accuracy of fault estimation. Finally, three examples are given to illustrate the effectiveness and merits of the proposed method.