The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered from complex tracking-error dynamics. Furthermore, techniques that are based on linear feedback strategies obtained at equilibrium conditions either may not hold or degrade when applied to uncertain dynamic environments. Pre-tuned fuzzy inference architectures lack robustness under such unmodeled conditions. This work introduces an adaptive distributed technique for the autonomous control of flock systems. Its relatively flexible structure is based on online fuzzy reinforcement learning schemes which simultaneously target a number of objectives; namely, following a leader, avoiding collision, and reaching a flock velocity consensus. In addition to its resilience in the face of dynamic disturbances, the algorithm does not require more than the agent position as a feedback signal. The effectiveness of the proposed method is validated with two simulation scenarios and benchmarked against a similar technique from the literature.

翻译：群集引导问题具有一个富有挑战性的结构，其中多个优化目标被同时求解。这通常需要采用不同的控制方法来应对各种目标，如引导、碰撞避免和凝聚力。特别是引导方案，长期受困于复杂的跟踪误差动力学。此外，基于平衡条件下获得的线性反馈策略的技术，在应用于不确定的动态环境时可能失效或性能下降。预调制的模糊推理架构在此类未建模条件下缺乏鲁棒性。本文提出了一种用于群集系统自主控制的自适应分布式技术。其相对灵活的结构基于在线模糊强化学习方案，该方案同时针对多个目标，即跟随领导者、避免碰撞以及达成群集速度共识。除了对动态干扰具有鲁棒性外，该算法仅需代理位置作为反馈信号。通过两个仿真场景验证了所提方法的有效性，并与文献中的类似技术进行了基准对比。