Learning-based target fencing control for delay-tolerant unmanned aerial vehicle swarm

This study focuses on the cooperative fencing mission for unmanned aerial vehicle (UAV) swarm under communication delays, proposing an adaptive self-organized control framework based on a Radial Basis Function-Brain Emotional Learning-Based Intelligent Controller (RBF-BELBIC). Firstly, a fixed-time convergent observer is developed to realize simultaneous estimation of multiple states of the target, achieving precise estimation independent of initial states through dual-channel Hurwitz polynomial configuration. Secondly, a self-organized distributed control scheme integrating consensus term, navigation term, and potential field term is constructed. This strategy enables the UAV swarm to autonomously generate a dynamic fencing convex hull around the target, eliminating the dependency on predefined geometric configurations while guaranteeing collision avoidance. Thirdly, a dual-layer intelligent robust controller driven by the RBF-BELBIC network is designed to tackle the control lag effects caused by communication delays. This architecture establishes a hierarchical structure where the RBF network serves as an upper layer for online gain optimization, and the BELBIC acts as a lower reactive control layer, thereby enabling simultaneous disturbance compensation and dynamic control policy adaptation. Closed-loop stability is analytically established using Lyapunov theory. Simulations verify that the proposed control strategy extends the tolerable delay bound by an order of magnitude over conventional methods (from 100 ms to 1000 ms). Concurrently, it reduces fencing position and velocity errors by 99.36% and 97.45%, compared to single-layer learning networks under large delays, demonstrating superior robustness in complex environments.