This paper is concerned with the fault reconstruction observer design problem with unknown nonlinearities, external disturbances and faults. First, a neural-network-based fault estimation approach is developed to generate the estimations of the actuator failures. In this design, the neural network strategy is utilized to approximate the totally unknown nonlinear functions. Then, an iterative adaptive observer is designed to offer the accurate estimations of the sensor faults, where the estimations in the previous iteration are applied in the current iteration to guarantee the convergence of sensor fault estimation errors. The developed neural network observer design approach can reconstruct the states, actuator and sensor faults for the unmanned surface vessel simultaneously. Finally, a practical example of the unmanned surface vessel is presented to illustrate the effectiveness and the potential of the proposed observer technique.

中文翻译：

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使用神经网络观察者方法对无人水面船只进行自适应故障估计

本文涉及具有未知非线性、外部干扰和故障的故障重建观测器设计问题。首先，开发了一种基于神经网络的故障估计方法来生成执行器故障的估计。在这个设计中，神经网络策略被用来逼近完全未知的非线性函数。然后，迭代自适应观测器被设计为提供传感器故障的准确估计，其中在当前迭代中应用前一次迭代中的估计以保证传感器故障估计误差的收敛。开发的神经网络观察器设计方法可以同时重建无人水面舰艇的状态、执行器和传感器故障。最后，