Takagi-Sugeno Fuzzy Observer Design for Induction Motors with Immeasurable Decision Variables: State Estimation and Sensor Fault Detection

paper deals with the problem of sensor fault detection of induction motors described by some linear models blended together through non linear membership functions that involve unmeasurable decision variables. The intermittent disconnections of the sensors produce severe transient errors in the estimator used in the control loop, worsening the performance of the induction motor. Then, a Takagi-Sugeno (TS) observer is proposed, in descriptor form, to simultaneously estimate the states and achieve the detection and isolation of incipient sensors faults. For this, a TS model is first derived to represent precisely the induction motor in the fixed stator d-q reference frame. Secondly, a descriptor TS observer is synthesized, in which the sensor faults are considered as an auxiliary variable state. Some simulation results illustrate the effectiveness of the proposed approach