In this paper, a neural modified extended state observer (ESO) (NMESO) based on the compound neural orthogonal network (CONN) is proposed to improve the online estimation of the system uncertainties and the exogenous disturbances in the uncertain system. In the developed scheme, CONN is designed as the main estimator to identify the total disturbances and facilitate the estimation of the conventional ESO. The convergence of NMESO is proved in time domain and the estimation errors are shown to be bounded. Due to the orthogonality of hidden nodes and the iterative updating mechanism of CONN, the estimation accuracy, rapidity, and real-time performance of NMESO have been greatly enhanced. Furthermore, the cooperative mechanism of the CONN estimator and the conventional ESO in the developed NMESO is also studied. Finally, the effectiveness of the proposed method and the results of analysis are verified through comparative simulation experimentation. This paper depicts a promising prospect of the NMESO in application of practical engineering.

中文翻译：

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通过分析和实验验证神经修改扩展状态观察器

本文提出了一种基于复合神经正交网络（CONN）的神经改进扩展状态观测器（ESO）（NMESO），用于改进系统不确定性和不确定系统中外生扰动的在线估计。在所开发的方案中，CONN 被设计为主要估计器，以识别总干扰并促进常规 ESO 的估计。NMESO 的收敛性在时域得到证明，估计误差是有界的。由于隐藏节点的正交性和 CONN 的迭代更新机制，NMESO 的估计精度、快速性和实时性都得到了极大的提升。此外，还研究了开发的 NMESO 中 CONN 估计器和常规 ESO 的协作机制。最后，通过对比仿真实验验证了所提方法的有效性和分析结果。本文描绘了 NMESO 在实际工程应用中的广阔前景。