Neuroadaptive consensus learning for multi-agent systems: An incremental approach to nonstrict pure-feedback control

This paper investigates the distributed learning consensus control problem for nonstrict pure-feedback multi-agent systems using neural networks and an incremental adaptive mechanism. A unified adaptive learning consensus control framework is first established by integrating backstepping techniques with neural network approximation. To address the algebraic loop problem inherent in conventional approaches, we develop a neural network-based solution that simultaneously simplifies controller architecture. The proposed incremental adaptation strategy enables efficient parameter updating while significantly reducing computational overhead. Notably, the control scheme incorporates robustness analysis during the design phase to effectively resolve the complexity explosion issue. Theoretical analysis demonstrates that the distributed protocol guarantees prescribed tracking performance while ensuring the uniform boundedness of all closed-loop signals. The numerical case studies validate the effectiveness and learning capabilities of the proposed adaptive control algorithm.