Fixed-time adaptive consistent control of higher-order nonlinear multi-agent systems with full state constraints and input saturation

This paper investigates high-order nonlinear multi-agent systems with state constraints and input saturation. A novel control scheme incorporating Neural Networks and Barrier Lyapunov Functions is designed to achieve adaptive fixed-time consensus control. This innovative scheme effectively addresses the complexity explosion problem typical in traditional controller designs while ensuring that the closed-loop system remains within its constraints. During the design process, a first-order sliding mode differentiator was introduced, and compensations were made for filter errors to ensure stability and consistency within a fixed-time. Additionally, experiments using Matlab numerical simulations and the StarSim semi-physical simulation platform confirm that the proposed control scheme significantly surpasses traditional methods in efficiency and accuracy, validating its effectiveness and practicality for solving the consensus problem in high-order nonlinear multi-agent systems. • Introduces NN-based fixed-time adaptive control for uncertain nonlinear MASs. • Incorporates BLF backstepping with sliding-mode differentiator for state constraint. • Adds filter error compensation for bounded closed-loop system stability. • Adaptive inputs saturation limits, ensuring bounded input signals and system stability.