Distributed optimal control algorithms have been used as the foundational framework for adaptive error management in ultra-dense 6G sensor network deployments, enabling efficient data transmission and robust error resilience. In the specific scenario of ultra-dense 6G networks, characterized by high uncertainties in both signal acquisition and communication channels, traditional fixed-threshold schemes fail to mitigate error clustering and achieve rapid convergence. In this paper, we propose a distributed adaptive stability control algorithm (DASCA) based on the distributed Hamilton-Jacobi-Bellman optimal control framework to address these limitations. Our approach integrates an enhanced consensus update protocol with higher-order compensation and adaptive step-size strategies, significantly improving convergence rate and error minimization. Experimental results demonstrate that the DASCA reduces the cost functional by approximately 50% and increases the convergence rate by around 60%, thereby outperforming conventional methods and confirming its efficacy in rapid error mitigation.

Autonomous vehicle fleets have been used as the communication infrastructure basis for information dissemination in cooperative autonomous driving scenarios such as urban intersections, toll plazas, and congested highways. In the specific scenario of highly dynamic and localized environments, existing dissemination methods suffer from scalability, latency, and reliability issues. In this paper, we propose an enhanced wavefront-based information dissemination (WBID) scheme to address the inefficient and unreliable propagation of critical information. The scheme integrates improved diffusion models with a hybrid robust control-based distributed optimization (HRCDO) mechanism and an adaptive multi-hop communication (AMHC) mechanism to jointly regulate spatiotemporal information propagation speed, relay decision robustness, and communication efficiency so that information dissemination can remain low latency, high coverage, and reliable under rapidly varying vehicle density and environmental uncertainty. Experimental results demonstrate that the proposed WBID scheme, utilizing the advanced multi-objective relay optimization algorithm (AMOROA), achieves a 95% information coverage, reduces dissemination time by 40%, and lowers communication overhead by 25% compared to existing methods.

The Susceptible-Infected-Recovered paradigm (SIR) coupled with decentralized gossip remains central to information flow and misinformation mitigation in heterogeneous urban networks, yet static parameters (β, γ, τ) and limited adaptivity hinder responsiveness under rapidly varying conditions. We propose the stochastic trust-enhanced calibration algorithm (STCA), which unifies dynamic trust updates with adaptive gossip control under a high-order Hamilton-Jacobi-Bellman (HJB) stochastic optimization framework. At its core, the STCA performs iterative calibration of (β, γ, τ) via gradient descent augmented by fractal-inspired performance metrics and recursive coupling operators, yielding robust stability guarantees and real-time adaptation to mobility and load fluctuations. Experiments demonstrate a 50 % reduction in detection time (15 s vs. 30 s), an isolation-efficiency gain of approximately 19 % (0:95 vs. 0:80), and a 50 % increase in communication throughput (1200 units vs. 800 units). These results indicate that jointly enhancing trust dynamics, stochastic control, and adaptive gossip via STCA constitutes a scalable technological advancement for smart-city information dissemination, delivering certificate-oriented stability and performance suitable for next-generation (6G+) deployments.