Memory-Based Resilient Control Against Non-cooperation in Multi-agent Flocking

Inspired by natural flocking behaviors, researchers aim to develop a distributed control approach for artificial agents to mimic these behaviors. The main challenge lies in maintaining the resilience of the artificial flock, as some agents inevitably display non-cooperative behavior, thereby deviating from the flocking objective. Existing control approaches, especially those based on learning algorithm, are susceptible to forgetting issues that non-cooperative agents can exploit to disrupt the flock formation. To address this problem, this study introduces a memory-based resilient control approach that strategically analyzes historical data across three distinct time scales (long, short, and periodic). The implementation of a long short periodic-term memory (LSP) algorithm employs accumulative discounted credibility evaluated by Q-learning to recognize long-term non-cooperation, utilizes a filtering rule to establish a trusted set excluding short-term non-cooperation, and integrates fast Fourier transform to refine the trusted set against periodic inconsistency. We assess the effectiveness of this approach through extensive experiments. The results highlight the potential and advantages of using LSP in flocking, enhancing the resilience of multiagent flocking against complex non-cooperative threats.