This paper investigates the H∞ filtering problem for Takagi-Sugeno fuzzy impulsive switched systems (TSFISSs) with multipath packet dropouts through hybrid strategies and constructing novel dynamic observer-based filters. The multipath packet dropout phenomenon refers to packet dropouts that occur in both the measurable output channel and the performance output channel, which are respectively characterized by two mutually independent Bernoulli random sequences. Packet dropouts may prevent the filters from acquiring the switching information of the system. To tackle this problem, hybrid strategies are adopted in this paper, in which mode-dependent and mode-independent filters are designed simultaneously. By integrating the average dwell time (ADT) switching strategy with the multiple Lyapunov functions framework, sufficient criteria ensuring the existence of the prescribed filters are established via linear matrix inequalities (LMIs). Under the designed filters, the filtering error systems achieve exponential mean-square stability and satisfy a weighted H∞ performance index. Finally, a practical example is provided.
This paper investigates distributed observer-based event-triggered control for on-board power systems via a hierarchical cooperative method. Precise voltage regulation and current sharing in on-board power systems are essential to ensure stability under load transients, prevent cascading faults, and optimize efficiency through coordinated power electronics. Firstly, converters are modeled as cyber agents, with distributed voltage observers reducing the coupled nonlinear dynamics to first-order integrators, thereby transforming voltage and current regulation into a linear consensus problem without relying on centralized global information. Additionally, different from conventional fixed-threshold event-triggered schemes, a dynamic event-triggered scheme adaptively adjusts triggering parameters according to current and voltage deviations, enabling rapid response to critical events while reducing unnecessary communication. Consequently, a hierarchical distributed cooperative control law based on nearest-neighbor information is developed to achieve coordinated voltage regulation and current sharing while enhancing robustness against converter and communication-link failures. Furthermore, sufficient conditions to ensure closed-loop system stability, voltage regulation, and current sharing are rigorously established through the Lyapunov stability theorem. Extensive simulation results further confirm the effectiveness and fault-tolerant performance of the proposed method under component and connectivity failures.
The accuracy of sheet metal forming simulations is highly dependent on the selected yield criterion. However, most existing criteria lack flexible control over yield surface curvature, thereby limiting their ability to capture the anisotropy under complex biaxial loading. To address this, a novel stress-triaxiality dependent anisotropic-asymmetric yield criterion is proposed under non-associated flow rule. The new model can be reduced to an isotropic form that allows for the broadest adjustment of yield surface curvature under near-plane strain stress state among existing isotropic criteria. The anisotropy coefficients of the new yield criterion can be determined analytically from experimental data. The model was validated across various sheet metals, including high-strength steel, aluminum alloy, magnesium alloy, and titanium alloy. Compared with the evaluated advanced yield criteria, the proposed model achieves superior prediction accuracy for yield stresses under six distinct stress states and characterizing the anisotropic hardening behavior. Finally, the new model was implemented in ABAQUS for V-bending and tube hydroforming simulations. Finite element results show that the new model accurately predicts both V-bending involving tensile and compressive stress paths and tube hydroforming spanning a wide range of stress states.
This paper proposes a Heavy-Ball Random Reshuffling (HBRR) method, which extends the standard Random Reshuffling (RR) algorithm by incorporating the classical Heavy-Ball momentum mechanism. We systematically analyze the convergence properties of HBRR under constant step sizes for strongly convex, general convex, and nonconvex finite-sum optimization problems. For strongly convex objectives, we prove linear contraction of the expected squared distance to a step-size-dependent residual neighborhood. For general convex objectives, we derive a sublinear convergence bound for the function-value error of the averaged iterates. For nonconvex objectives, we obtain a complexity bound of the same order as RR. Under the Polyak–Łojasiewicz condition, the expected function-value error contracts linearly to a residual neighborhood and converges exactly when the residual vanishes. The theoretical framework is consistent, as it reduces precisely to the standard RR convergence results when the momentum parameter is zero. Numerical experiments on four benchmark datasets demonstrate that HBRR achieves competitive or superior performance in terms of convergence speed, solution accuracy, and algorithmic stability across all tested scenarios, confirming the effectiveness of incorporating Heavy-Ball momentum into the random reshuffling framework.
The incorporation of infinite-dimensional Wiener noise plays a crucial role in describing spatiotemporal stochastic fluctuations. However, existing studies rarely integrate this noise into reaction-diffusion epidemic models to examine spatial heterogeneity, which limits the understanding of the associated dynamic behaviors. To address this issue, this study investigates the nonlinear dynamics of a reaction-diffusion SIR model driven by infinite-dimensional Wiener noise. We establish well-posedness of mild solution within a semigroup framework and derive sufficient conditions for the persistence and extinction of the infected population in the sense of probability expectation. A higher-order positivity-preserving numerical scheme not only ensures biologically meaningful simulations but also suggests possible variation trends of the stationary distribution with respect to spatial heterogeneity and noise. The analysis of the marginal probability density functions (MPDFs) of infected individuals indicates that increasing noise intensity accelerates disease extinction, and spatial heterogeneity (manifested in significant density differences at different locations) provides a basis for identifying high-risk areas. Comparative results show that infinite-dimensional Wiener noise is more effective than Wiener noise in capturing spatial stochastic structures. This study contributes to a deeper understanding of spatially structured epidemic dynamics and provides insights for the development of more refined intervention strategies.
