This paper presents an Unknown Input robust Observer (UIO) capable of simultaneously estimate both sensor fault and system states. The system is assumed to be discrete-time Takagi-Sugeno (T-S) Fuzzy with uncertainties. An augmented system is obtained from the dynamic fault model and original system. Afterward, an UIO is designed for the augmented system aiming at decoupling process disturbances. Its design is obtained by using an H_∞ optimization technique and developed to maintain the observer stable, reducing the non-decoupled process disturbances effect. The proposed method is validated by two numerical examples as it is compared to a regular UIO technique and the extended Kalman filter. Results show the proposed technique presents better performance when the dynamic system is not purely nonlinear even if the same tuning parameters are chosen. Although others techniques are not able to ensure the error limitation, the proposed one is capable of it even in nonlinear systems.

本文介绍了一种能够同时估计传感器故障和系统状态的未知输入鲁棒观测器（UIO）。假设该系统是具有不确定性的离散时间Takagi-Sugeno（TS）模糊。从动态故障模型和原始系统获得增强系统。之后，针对扩展系统设计了一个UIO，旨在消除过程干扰。通过使用所获得的它的设计ħ _∞优化技术并开发以保持观察者稳定，减少了非解耦过程的干扰效应。通过将其与常规UIO技术和扩展卡尔曼滤波器进行比较，通过两个数值示例验证了该方法的有效性。结果表明，即使选择了相同的调整参数，当动态系统不是纯粹的非线性时，所提出的技术也具有更好的性能。尽管其他技术不能确保误差限制，但是即使在非线性系统中，所提出的技术也能够做到这一点。