Takagi-Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation with Application to Wind Turbines

In response to the high demand of the operation reliability by implementing real-time monitoring and system health management, a robust fault estimation and fault-tolerant control approach is proposed for Takagi– Sugeno fuzzy systems in this study, by integrating the augmented system method, unknown input fuzzy observer design, linear matrix inequality optimization, and signal compensation techniques. Specifically, a fuzzy augmented system method is used to construct an augmented plant with the concerned faults and system states being the augmented states. An unknown input fuzzy observer technique is thus utilized to estimate the augmented states and decouple unknown inputs that can be decoupled. A linear matrix inequality approach is further addressed to ensure the global stability of the estimation error dynamics and attenuate the influences from the unknown inputs that cannot be decoupled. As a result, the robust estimates of the concerned faults and system states can be obtained simultaneously. Based on the fault estimates, a signal compensation scheme is developed to remove the effects of the faults on the system dynamics and outputs, leading to a stable dynamic satisfying the expected performance. Finally, the effectiveness of the proposed Takagi–Sugeno model based fault estimation and signal compensation algorithms is demonstrated by a case study on a 4.8-MW wind turbine benchmark system.