Robustness Enhancement of Consensus Networks: the Optimal Memory Depth
For researchers in network science and multi-agent systems, this work provides a theoretical understanding of how memory depth affects collective robustness, though the findings are incremental and specific to the proposed protocol.
This paper investigates how local memory depth enhances the robustness of multi-agent consensus networks, quantified by the H2 performance metric. It shows that memory at any depth improves performance, with the optimal depth being either the most recent or most remote memory depending on parameter regions.
Understanding what governs collective robustness and how it can be enhanced remains a central pursuit in network science. This paper investigates the robustness of multi-agent consensus networks, quantified by the $H_2$ performance metric, and delves into the enhancing effect of agents' local memory on it. Inspired by the hierarchical temporal structure of memory observed in neuroscience, we focus on the role of memory depth, which reflects the temporal features of memory from recent to remote. Building on linear extrapolation, we propose a consensus protocol with single-step memory and tunable memory depth, derive the necessary and sufficient condition for achieving consensus, and show that the protocol exhibits an inheritable consensus property across memory depths. Furthermore, analytical expressions for the $H_2$ performance metric, which depend on the memory factor, memory depth, coupling gain, and Laplacian spectrum, are established. Under balanced usage of real-time and memory information, we demonstrate that memory at any accessible depth enhances $H_2$ performance, and the optimal memory depth occurs at either the most recent or the most remote memory, contingent upon certain parameter regions. Further detailed discussions are provided to clarify the broader implications of our findings.