This article presents a novel unknown input observer (UIO)-integrated extended finite impulse response (EFIR) estimator and its application for an effective sensor fault-tolerant control (FTC) of an electrohydraulic actuator (EHA). The proposed estimator exploits the UIO structure in the EFIR filter. Thus, it requires only a small amount of historical data ( $N$ ) while ensuring the following: 1) sensor fault and system state estimation accuracy under time-correlated noise; 2) the number of estimator design parameters is significantly minimized; and 3) robust residual generation. A Lyapunov-stability-based theory is carried out to study its convergence condition. Next, an EHA-based test rig has been set up, and sensor FTC is performed by carrying this estimator as part of a fault diagnosis algorithm to evaluate its performance by both simulation and real-time experiments. Results highlight that under optimal setting ( $N=N_{\rm opt}$ ), the estimator performance is near accurate to the very well developed extended-Kalman-filter-based UIO in undisturbed conditions but significantly outperforms when dealing with time-correlated noise under the same control environment. The estimator also shows its robustness under below-optimal setting (downgrading $N_{\rm opt}$ by 50%) while performing sensor FTC in real time.

中文翻译：

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传感器容错控制应用中电动液压执行器的未知输入观测器-EFIR组合估计器

本文介绍了一种新颖的未知输入观测器（UIO）集成的扩展有限脉冲响应（EFIR）估计器及其在电动液压执行器（EHA）的有效传感器容错控制（FTC）中的应用。提出的估算器利用了EFIR滤波器中的UIO结构。因此，它只需要少量的历史数据（ $ N $ ）同时确保以下几点：1）时间相关噪声下的传感器故障和系统状态估计精度；2）估计器设计参数的数量大大减少；3）强大的残差生成。基于李雅普诺夫稳定性理论研究其收敛条件。接下来，已经建立了一个基于EHA的测试平台，并通过将该估计器作为故障诊断算法的一部分来执行传感器FTC，以通过仿真和实时实验评估其性能。结果突出表明在最佳设置下（ $ N = N _ {\ rm opt} $ ），在不受干扰的条件下，估算器的性能几乎与非常完善的基于扩展卡尔曼滤波器的UIO一样准确，但在相同控制环境下处理与时间相关的噪声时，估算器的性能明显优于同类产品。估算器还显示了在低于最佳设置（降级）下的鲁棒性$ N _ {\ rm opt} $ 50％），同时实时执行传感器FTC。