This study proposes a novel artificial intelligence (AI) driven flight computer, integrating an online free-retraining-prediction model, a swarm control, and an obstacle avoidance strategy, to track dynamic targets using a distributed drone swarm for military applications. To enable dynamic target tracking the swarm requires a trajectory prediction capability to achieve intercept allowing for the tracking of rapid maneuvers and movements while maintaining efficient path planning. Traditional predicative methods such as curve fitting or Long ShortTerm Memory (LSTM) have low robustness and struggle with dynamic target tracking in the short term due to slow convergence of single agent-based trajectory prediction and often require extensive offline training or tuning to be effective. Consequently, this paper introduces a novel robust adaptive bidirectional fuzzy brain emotional learning prediction (BFBEL-P) methodology to address these challenges. The controller integrates a fuzzy interface, a neural network enabling rapid adaption, predictive capability and multi-agent solving enabling multiple solutions to be aggregated to achieve rapid convergence times and high accuracy in both the short and long term. This was verified through the use of numerical simulations seeing complex trajectory being predicted and tracked by a swarm of drones. These simulations show improved adaptability and accuracy to state of the art methods in the short term and strong results over long time domains, enabling accurate swarm target tracking and predictive capability.

翻译：本研究提出了一种新型人工智能驱动的飞行计算机，它集成了在线免重训练预测模型、群体控制策略以及避障机制，旨在利用分布式无人机集群实现军事应用中的动态目标跟踪。为实现动态目标跟踪，集群需具备轨迹预测能力以实现拦截，从而在保持高效路径规划的同时跟踪目标的快速机动与移动。传统预测方法（如曲线拟合或长短期记忆网络）鲁棒性较低，且由于单智能体轨迹预测收敛速度慢，在短期动态目标跟踪中表现不佳，通常需要大量离线训练或调参才能生效。为此，本文提出了一种新颖的鲁棒自适应双向模糊大脑情感学习预测方法以应对这些挑战。该控制器融合了模糊接口、支持快速自适应的神经网络、预测能力以及多智能体求解机制，能够聚合多种解决方案以实现快速收敛，并在短期与长期预测中均获得高精度。通过数值仿真验证了无人机集群对复杂轨迹的预测与跟踪能力。仿真结果表明，该方法在短期内相比现有先进技术具有更强的适应性与准确性，在长时间域内亦表现出优异性能，从而实现了精确的集群目标跟踪与预测能力。