使用微分器的通用逼近及其在反馈控制中的应用
Universal approximation using differentiators and application to feedback control
AI总结：
针对一类状态不完全已知的非线性系统，提出基于积分链微分器或扩展观测器的通用逼近方法，同时实现不确定性逼近和未知状态估计，并有效抑制噪声。
摘要：
本文考虑了一类状态不完全已知的非线性系统的不确定性逼近和反馈控制问题，提出了两种逼近方法：使用积分链微分器或扩展观测器的通用逼近。与模糊系统和径向基函数（RBF）神经网络的逼近相比，所提出的两种方法不仅能通用地逼近不确定性，还能估计未知状态。此外，积分链微分器可以彻底抑制噪声。理论结果通过反馈控制的计算机仿真得到了验证。
英文摘要：
In this paper, we consider the problems of approximating uncertainties and feedback control for a class of nonlinear systems without full-known states, and two approximation methods are proposed: universal approximation using integral-chain differentiator or extended observer. Comparing to the approximations by fuzzy system and radial-based-function (RBF) neural networks, the presented two methods can not only approximate universally the uncertainties, but also estimate the unknown states. Moreover, the integral-chain differentiator can restrain noises thoroughly. The theoretical results are confirmed by computer simulations for feedback control.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
