基于带扰动消除的DREM的非线性系统状态自适应重构
Adaptive Reconstruction of Nonlinear Systems States via DREM with Perturbation Annihilation
AI总结：
针对含未知输入和参数不确定性的非线性系统，提出基于DREM和扰动消除算法增强高增益观测器的自适应观测器，确保参数、状态和扰动估计渐近收敛至平衡点任意小邻域，并通过数值实验验证。
摘要：
针对具有有界未知输入和参数不确定性的特定非线性系统类，提出了一种新的自适应观测器。与现有大多数解决方案不同，该方法确保未知参数、状态和扰动估计渐近收敛至平衡点的任意小邻域。该方案基于高增益观测器的新颖扩展，并结合了通过扰动消除算法增强的动态回归器扩展与混合（DREM）过程。上述特性已通过数值实验验证。
英文摘要：
A new adaptive observer is proposed for a certain class of nonlinear systems with bounded unknown input and parametric uncertainty. Unlike most existing solutions, the proposed approach ensures asymptotic convergence of the unknown parameters, state and perturbation estimates to an arbitrarily small neighborhood of the equilibrium point. The solution is based on the novel augmentation of a high-gain observer with the dynamic regressor extension and mixing (DREM) procedure enhanced with a perturbation annihilation algorithm. The aforementioned properties of the proposed solution are verified via numerical experiments.
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。
