发表机构
Concordia University（康考迪亚大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对一类非线性单侧Lipschitz系统的回归器失配问题，提出一种基于有限激励估计状态回归器的自适应观测器，经稳定性分析其性能优于无历史堆叠学习项的观测器。
AI 中文摘要
针对具有未知参数、有限激励的系统，并发学习可在无持续激励下实现参数收敛，但回归器仍可能依赖不可达状态，导致回归器失配。本文针对一类具有单侧Lipschitz特性、二次内有界非线性项、有界扰动及线性参数化不确定性的非线性系统，解决该问题。为此，利用测量输出和估计状态构造输出积分回归，明确将历史堆叠残差界定于状态估计误差和扰动；推导估计状态与真实状态信息矩阵间的扰动界；采用OSL-QIB线性矩阵不等式（LMI）条件进行观测器设计，并设计投影自适应律，无需精确输出匹配。稳定性分析表明，所提观测器及参数估计性能优于无历史堆叠学习项的观测器。
英文摘要
For systems with unknown parameters, finite excitation and concurrent learning can potentially yield parameter convergence without persistent excitation but the regressor may still depend on inaccessible states, leading to regressor mismatch. In this paper, this problem is addressed for a class of nonlinear systems with one-sided Lipschitz properties and quadratically inner-bounded nonlinearities with bounded disturbances and linearly parametrized uncertainties. To this aim, an output-integral regression is utilized by using measured outputs and estimated states, and history-stack residual is explicitly bounded in terms of state-estimation error and disturbance. Furthermore, a perturbation bound between the estimated-state and true-state information matrices is derived. Additionally, an OSL-QIB LMI condition is applied for the observer design and a projected adaptive law is designed without needing exact output matching. Stability analysis's results indicate the proposed observer and parameter estimation outperform observers without history-stack learning term.
