AI 中文总结
研究离散时间非线性系统在有向通信网络上的分布式自适应状态估计，提出归一化自适应估计方案，通过李雅普诺夫分析建立稳定性条件，经数值模拟验证，该估计器能准确估计且随网络规模有效扩展。
AI 中文摘要
本文研究了具有未知源动态的离散时间非线性系统在有向通信网络上的分布式自适应状态估计。每个传感代理仅使用本地测量和与相邻代理交换的信息来估计源状态，实现了无需共享激励或控制输入的完全分布式实现。提出了一种归一化自适应估计方案，用于识别未知的线性和非线性动态，同时确保鲁棒离散时间自适应。基于李雅普诺夫的分析建立了估计误差动态的输入到状态稳定性（ISS），保证了在有界干扰下自适应参数有界，以及在无干扰情况下在合适条件下估计误差的渐近收敛。为了表征网络诱导的耦合，为耦合算子开发了基于显式范数和基于LMI的舒尔稳定性条件，包括考虑有界模型不确定性的鲁棒公式。在星形、循环和路径通信拓扑上的数值模拟证明了准确的分布式状态估计，并验证了所提出的稳定性条件。计算结果进一步表明，所提出的估计器能随网络规模有效扩展。
英文摘要
This paper studies distributed adaptive state estimation for discrete-time nonlinear systems with unknown source dynamics over directed communication networks. Each sensing agent estimates the source state using only local measurements and information exchanged with neighboring agents, enabling a fully distributed implementation without requiring shared excitation or control inputs. A normalized adaptive estimation scheme is proposed to identify unknown linear and nonlinear dynamics while ensuring robust discrete-time adaptation. A Lyapunov-based analysis establishes input-to-state stability (ISS) of the estimation error dynamics, guaranteeing bounded adaptive parameters under bounded disturbances and asymptotic convergence of the estimation errors in the disturbance-free case under suitable conditions. To characterize the network-induced coupling, explicit norm-based and LMI-based Schur stability conditions are developed for the coupling operator, including a robust formulation accounting for bounded model uncertainty. Numerical simulations on star, cyclic, and path communication topologies demonstrate accurate distributed state estimation and validate the proposed stability conditions. Computational results further show that the proposed estimator scales efficiently with the network size.
CommentsThis is an extended version of the paper accepted for publication in ASME Letters in Dynamic Systems and Control
