Fault detection for nonlinear networked systems based on quantization and dropout compensation: An interval type-2 fuzzy-model method

This paper investigates the problem of filter-based fault detection for a class of nonlinear networked systems subject to parameter uncertainties in the framework of the interval type-2 (IT2) T–S fuzzy model-based approach. The Bernoulli random distribution process and logarithm quantizer are used to describe the measurement loss and signals quantization, respectively. In the framework of the IT2 T–S fuzzy model, the parameter uncertainty is handled by the membership functions with lower and upper bounds. A novel IT2 fault detection filter is designed to guarantee the residual system to be stochastically stable and satisfy the predefined H ∞ performance. It should be mentioned that the proposed filter does not use the same premise variables, number of fuzzy rules and membership functions as the fuzzy model, which will lead to more flexible design. Finally, two illustrative examples are provided to demonstrate the usefulness of the approach proposed in this paper.