Decentralized coordination of multi-agent communication networks underpins autonomous UAV swarms, sensor meshes, and cyber-physical systems that must operate without a single point of failure, yet quantitative design rules linking topology degradation to coordination collapse are still lacking. This paper aims to characterize the fault tolerance of a discrete linear consensus protocol that drives mobile agents’ scalar states toward a common value over a time-varying communication graph. The convergence rate and fault tolerance budget of the protocol are explicitly tied to the algebraic connectivity ${\lambda }_2$ of the graph Laplacian, and three failure regimes—random independent failures ($M_1$), sequential staged failures ($M_2$), and targeted attacks on the highest-degree hubs ($M_3$)—are analyzed using a full-factorial Monte-Carlo experiment of 1620 runs implemented in Python with the Mesa framework and NetworkX. Critical failure thresholds ${\phi }_c$ are 0.42–0.51 for $M_1$, 0.38–0.48 for $M_2$, and 0.28–0.38 for $M_3$; a Kruskal–Wallis test (p < 0.001) confirms the ordering ${\phi }_c^{M^3}<{\phi }_c^{M^2}<{\phi }_c^{M^1}$, providing quantitative design rules for fault-tolerant consensus-driven networks.

Accurate dynamic modeling is the foundation of every model-based control design for reaction-wheel stabilized systems, yet most published studies reproduce analytical derivations from scratch without reporting the practical decisions and configuration errors that arise when the model is implemented in Simscape Multibody. This paper presents a reproducible methodology for building a high-fidelity model of a Cubli-type self-balancing cube, proceeding from a SolidWorks 2024 CAD assembly to a linearized third-order state-space representation ready for controller synthesis. Three implementation pitfalls that are rarely discussed in the literature are identified: the placement of the CAD origin, which determines whether the imported model behaves as a stable compound pendulum or as the intended inverted pendulum; the default angular units in the PS-Simulink Converter, which silently degrade any control law tuned for radians; and the kinematic realization of the edge contact. The resulting linearized model is validated through eigenvalue analysis, a controllability rank test, and a short LQR stabilization benchmark. The methodology is directly transferable to reaction-wheel-stabilized satellites and other underactuated rigid-body platforms that share the same modeling workflow.