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Zonotopic Kalman Observer-based Sensor Fault Estimation for Discrete-Time Takagi-Sugeno Fuzzy Systems

This paper focuses on sensor fault estimation for discrete-time Takagi-Sugeno fuzzy systems with unknown process and measurement noises. First, the original T-S system is transformed into an augmented one to estimate the fault and system state simultaneously. A Zonotopic Kalman Observer is then designed by combing the structure of Kalman observer and zonotope theory. In addition, we propose a gain optimality method by minimizing the Frobenius norm of zonotopes. Finally, A truck-trailer example is given to highlight the effectiveness of the proposed sensor fault estimation method.