Echo-state network based adaptive fuzzy sliding-mode consensus control scheme for nonlinear multi-agent systems with...

This paper presents a novel hybrid control strategy for nonlinear multi-agent systems (MASs) operating under external disturbances and system uncertainties. The proposed framework integrates an adaptive fuzzy sliding-mode (AFSM) controller, an echo state network (ESN) optimized by the chaotic whale optimization algorithm (CWOA), and a nonlinear disturbance observer unit (NDOU). While conventional sliding-mode control (SMC) ensures robustness and simplicity, it often suffers from chattering that can degrade actuator performance. The AFSM component effectively mitigates chattering while leveraging the fuzzy system's approximation capability to handle bounded unknown dynamics. The NDOU is incorporated to estimate and compensate for unmeasured internal states and time-varying external disturbances, thereby enhancing disturbance rejection. Meanwhile, the ESN, optimized via CWOA, improves state estimation accuracy and dynamic adaptability. Through the synergistic integration of AFSM, ESN, and NDOU, the proposed scheme achieves fast, robust, and reliable consensus among agents while overcoming the limitations of conventional single-technique controllers. Lyapunov stability analysis is employed to rigorously verify system stability. Simulation results on two distinct nonlinear MAS scenarios confirm that the proposed hybrid controller enhances tracking performance, robustness, and control efficiency, demonstrating its practical effectiveness for advanced MAS applications.