BURA Collection:

Zonotopic Set-Membership Fusion Estimation for Multi-Sensor Systems Under FlexRay Protocol

2026-06-10T02:00:41Z

Title: Zonotopic Set-Membership Fusion Estimation for Multi-Sensor Systems Under FlexRay Protocol Authors: Zhao, Z; Wang, Z; Liang, J Abstract: Networked multi-sensor systems operating under the FlexRay protocol (FRP) are widely used in automotive industry, where reliable state estimation under bounded uncertainties is of fundamental importance. In such systems, the measurement information from multiple sensors is transmitted to the estimator through a network governed by the FRP, which induces scheduling constraints and switching behaviors in the estimation process. These characteristics make it challenging to guarantee accuracy and boundedness of the state estimates using conventional methods. This paper investigates the zonotopic set-membership fusion estimation (SMFE) problem for multi-sensor systems under the FRP. The research objective is to design a parallel fusion estimation algorithm for the transformed switched system, to establish a sufficient condition guaranteeing the ultimate boundedness of the radii of the resulting zonotopes, and to improve the transient estimation performance. An SMFE algorithm is proposed to recursively calculate the zonotopes that constrain the system state by exploiting the properties of zonotopes. A sufficient condition is derived to ensure the ultimate boundedness of the output zonotopes’ radii, which explicitly takes into account both the scheduling of the FRP and the adverse effect of zonotope order reduction on estimation performance. Furthermore, a matrix-inequality-based method is developed to construct an additional enclosing zonotope, based on which a tighter zonotope is obtained at each time instant to enhance the transient performance. The efficacy of the proposed SMFE method is demonstrated through two simulation experiments.

Distributed Fuzzy Proportional-Integral State Estimation Over Sensor Networks With Pull-Type Gossip Protocols and Fading Data

2026-06-10T02:00:37Z

Title: Distributed Fuzzy Proportional-Integral State Estimation Over Sensor Networks With Pull-Type Gossip Protocols and Fading Data Authors: Wang, Y; Wang, Z; Zou, L; Wang, F Abstract: This paper addresses the problem of distributed state estimation for smooth nonlinear systems over sensor networks by means of a generalized fuzzy proportional-integral observer (PIO). A sensor network is employed to collect system measurements, with a pull-type gossip protocol governing the intermittent data exchange among neighboring nodes. Under the gossip protocol, each sensor node randomly selects one neighbor to request data, facilitating distributed information updating. Furthermore, considering challenges such as long-distance communication and complex environmental conditions, signal transmission is subject to amplitude fading. To accommodate the characteristics of the gossip protocol, a generalized fuzzy PIO with a flexible structure is developed. Sufficient conditions are derived to guarantee the H__∞ estimation performance of the proposed observer. Based on established conditions, the parameters of both the gossip protocol and the fuzzy PIO are co-designed via a particle-swarm-optimization-based iterative algorithm, with emphasis on enhancing observer robustness. Finally, an engineering-oriented simulation example is presented to illustrate the effectiveness of the proposed methodology.

PID-Based Secure Cluster Synchronization of Discrete-Time Nonlinear IoT Networks Under Stochastic Replay Attacks

2026-06-10T02:00:38Z

Title: PID-Based Secure Cluster Synchronization of Discrete-Time Nonlinear IoT Networks Under Stochastic Replay Attacks Authors: Chen, Y; Wang, Z; Liu, Y; Song, W Abstract: Large-scale Internet of Things (IoT) systems are characterized by massive numbers of interconnected devices, heterogeneous dynamics, and complex interaction structures, which can be effectively modeled using complex networks. In many IoT applications, secure cluster synchronization is essential for coordinated and reliable operation, yet it is highly vulnerable to cyber-attacks, particularly replay attacks that maliciously reuse previously transmitted but valid data. This paper investigates the secure cluster synchronization problem for discrete-time nonlinear complex networks representing IoT systems under stochastic replay attacks. A probabilistic replay attack model with bounded consecutive attack duration is introduced to capture the random and intermittent characteristics of realistic attack behaviors. To mitigate the adverse impact of replayed information, a PID-based cluster synchronization control strategy is developed, where proportional, integral, and derivative actions are jointly exploited to enhance robustness against outdated and compromised signals. By constructing an appropriate Lyapunov functional and employing stochastic analysis techniques, sufficient conditions are derived to guarantee asymptotic mean-square cluster synchronization. A systematic controller synthesis procedure is further provided. Numerical simulations demonstrate the effectiveness and improved resilience of the proposed approach compared with conventional proportional control schemes.

SCMoE-PFL: A soft-clustering mixture-of-experts framework for personalized federated learning

2026-06-10T02:00:40Z

Title: SCMoE-PFL: A soft-clustering mixture-of-experts framework for personalized federated learning Authors: Li, G; Jia, X; Liu, W; Zhang, E; Wang, Z Abstract: Traditional federated learning (FL) methods rely on a single global model, which often collapses under heterogeneous and non-IID client data distributions. Personalized federated learning (PFL) alleviates this limitation, yet existing approaches either overfit to local data or fail to exploit shared knowledge effectively. To address these challenges, this paper presents SCMoE-PFL, a personalized federated learning framework that integrates soft clustering and a mixture-of-experts (MoE) mechanism to reconcile global generalization with local personalization. First, we introduce a multi-center threshold-based soft clustering (MCTC) method that enables clients to participate in multiple clusters, improving data utilization and cluster quality. Second, intra-cluster aggregation yields a set of expert models, while each client separately trains a private model on its high-sensitivity data, ensuring privacy preservation. Finally, a lightweight energy-aware gating network adaptively fuses expert and private models. By calibrating initial feature-matching weights with energy-based predictive confidence, this dual-check mechanism effectively prevents over-reliance on uncertain experts, thereby producing highly reliable personalized predictions. Experiments on four benchmark datasets demonstrate that SCMoE-PFL substantially improves accuracy, convergence, and fairness under both moderate and extreme heterogeneity, achieving maximum accuracy improvements of 24.71 and 26.01 percentage points over FedAvg, respectively. Theoretical analysis further establishes performance lower bounds and clarifies the framework’s advantages in privacy protection, computational efficiency, and system reliability. These results show that SCMoE-PFL offers a robust and flexible solution for personalized federated learning in heterogeneous environments. Description: Data availability: Data will be made available on request.