A Novel Approach to Fuzzy Fault Estimation for Sampled-data Nonlinear Systems

In this paper, a novel fuzzy fault estimation technique is proposed for nonlinear systems with sampled-data output. Based on a Takagi–Sugeno fuzzy model, the fuzzy fault estimation observer is considered and its \(H_{\infty }\) $H_{\infty }$ fault estimation performance problem is addressed. To conquer the limit of the approximate discretization approach, the sampled-data fault estimation condition with an \(H_{\infty }\) $H_{\infty }$ performance is guaranteed by using the continuous-time Lyapunov functional. Also, the sufficient condition of the fault estimation technique is converted into the linear matrix inequality format. Finally, a numerical example is provided to verify the effectiveness of the proposed fault estimation technique.