发表机构
Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM); Department of Mathematics and Statistics, University of New Mexico（胡志明市理工大学机械工程学院，越南国立大学胡志明市分校; 新墨西哥大学数学与统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种分布式自适应神经区间观测器，用于未知非线性系统，通过局部测量与相邻信息构建区间估计，并引入经验回放机制增强权重收敛，经非线性示例验证。
AI 中文摘要
本文针对具有局部不完全测量的未知非线性系统，开发了一种分布式自适应神经区间观测器。每个传感器节点利用其局部输出和相邻观测器信息构建状态的下界和上界估计，而未知的非线性动态由自适应神经模型逼近。一种分布式自适应机制保证了估计误差和权重误差的有界性，而一种协作实现保持了分量区间性质。为了在不要求持续激励的情况下增强神经权重的收敛性，在自适应律中引入了一种有限经验回放积分并发学习机制。对于非Metzler误差动态，引入了一种基于Sylvester的坐标变换以恢复Hurwitz-Metzler分布式实现。理论发展通过一个非线性分布式估计示例得到验证。
英文摘要
This paper develops a distributed adaptive neural interval observer for unknown nonlinear systems with locally incomplete measurements. Each sensor node constructs lower and upper state estimates using its local output and neighboring observer information, while unknown nonlinear dynamics are approximated by adaptive neural models. A distributed adaptation mechanism guarantees bounded estimation and weight errors, whereas a cooperative realization preserves the componentwise interval property. To enhance neural-weight convergence without requiring persistent excitation, a finite experience-replay integral concurrent-learning mechanism is incorporated into the adaptation law. For non-Metzler error dynamics, a Sylvester-based coordinate transformation is introduced to recover a Hurwitz--Metzler distributed realization. The theoretical developments are validated through a nonlinear distributed estimation example.
