This article is concerned with distributed adaptive resource allocation over general digraphs with resource-demand constraints. The central aim is to tackle two essential challenges in distributed resource allocation, namely, scalable implementation and anytime feasibility, ensuring continuous satisfaction of constraints. For this purpose, two novel fully distributed optimization algorithms, featuring sum-based and product-based schemes for adaptive gains, are first developed. It is shown that these algorithms offer several advantageous features in terms of fully distributed implementation without global knowledge and algorithm simplicity as well as anytime feasibility guarantees over existing methods. Notably, the incorporation of a double-layer adaptive control law with a damping term into each algorithm prevents the continuous growth of adaptive gains, thus avoiding excessively large system gain values and enhancing practical applicability in real-world scenarios. Furthermore, by constructing appropriate Lyapunov functions, rigorous convergence analysis confirms that both algorithms achieve the optimal resource allocation and global asymptotic convergence. Finally, several simulation case studies are conducted to validate the efficiency of the proposed algorithms.

The distributed energy management (DEM) is of significance for smart grids due to the growing concern over potential cyber threats. This study delves into a multiobjective DEM problem that encompasses economic and environmental costs, as well as transmission losses, while also considering the impact of persistent Denial-of-Service (DoS) attacks. To tackle this challenge, a novel distributed optimization algorithm over a digraph is proposed, leveraging a zeroth-order scheme to handle unavailable gradients and incorporating momentum terms to provide accurate descent directions. The theoretical analysis demonstrates the algorithm's ability to achieve a linear convergence rate while ensuring real-time maintenance of decision variables within the feasible domain. Building on this, a novel reputation-based resilient DEM framework is introduced to address scenarios involving persistent DoS attacks. This framework calculates a reputation index for each communication link to monitor its reliability. Considering the impact of attacked links on network connectivity and their reputation indexes, corresponding strategies are devised. Specifically, if an attacked link disrupts network connectivity and its reputation index falls below the threshold, a connectivity restoration optimization algorithm is activated to reconstruct links, minimizing communication costs and alleviating information congestion. Finally, the effectiveness of the proposed algorithm and framework is validated through numerical simulations.

The convergence rate and applicability to directed graphs with interaction topologies are two important features for practical applications of distributed optimization algorithms. In this article, a new kind of fast distributed discrete-time algorithms is developed for solving convex optimization problems with closed convex set constraints over directed interaction networks. Under the gradient tracking framework, two distributed algorithms are, respectively, designed over balanced and unbalanced graphs, where momentum terms and two time-scales are involved. Furthermore, it is demonstrated that the designed distributed algorithms attain linear speedup convergence rates provided that the momentum coefficients and the step size are appropriately selected. Finally, numerical simulations verify the effectiveness and the global accelerated effect of the designed algorithms.

This paper investigates the distributed formation tracking problem of two-layer multiple autonomous surface vehicle (multi-ASV) systems with multiple leaders from the perspective of node-to-node consensus tracking. The leader ASVs aim to achieve the desired formation according to the actual task requirements and are not affected by the follower ASVs. It is worth noting that the ASVs in the leader layer and the ASVs in the follower layer can be equipped with heterogeneous topologies. To this end, the formation control law for the leader ASVs and the node-to-node formation tracking control law for the follower ASVs are proposed in this paper, respectively. Moreover, it is demonstrated that the leader ASVs can realize the desired formation, and the follower ASVs can achieve the node-to-node formation tracking under the designed control laws through theoretical analysis. Finally, numerical simulations are conducted to verify the validity of the theoretical results.