A Distributed k ‐WTA Neural Network for Multi‐UAV Formation Stabilization

ABSTRACT The ‐winner‐take‐all (‐WTA) operation is a fundamental neural computation that models competitive selection among multiple agents. While existing ‐WTA networks have been extended to handle noise, unbalanced topologies and time delays, most approaches still suffer from high communication costs and limited ability to guarantee global selection optimality in distributed settings. To address these challenges, this paper develops a distributed ‐WTA neural selection‐control model that integrates nonlinear winner‐selection dynamics with communication‐efficient protocols. The proposed model ensures the convergence to globally optimal winners under local neighbour communication and provides rigorous stability guarantees. Building upon the selected winners, a cooperative formation control strategy with a double closed‐loop structure is designed, enabling fast convergence, robustness against disturbances and reduced communication burden. The effectiveness of the framework is validated through theoretical analysis, numerical simulations under both static and dynamic conditions, and a representative application to multi‐UAV cooperative competition and formation control. The results demonstrate that the proposed approach achieves global selection consistency, high‐precision tracking performance and strong robustness, thereby highlighting its potential as a general distributed neural framework for cooperation‐competition systems.