Finite-Time Neuro-adaptive Controller Algorithms for Nonlinear Multiagent Systems with State Constraints and Unmodeled Dynamics

Recently, the distributed adaptive algorithm design for multiagent systems has attracted considerable attention due to the fact that multiagent systems have shared broad application in many practical systems including unmanned aircraft clusters, intelligent robots, and intelligent transportation. However, most of the previous outcomes have overlooked the full-state constraints and unmodeled dynamics phenomenons, which widely existed in many practical scenarios. Hence, quite different from the existing literatures, the current investigation focuses on the finite-time neuro-adaptive controller algorithms design for nonlinear multiagent systems both confining with full-state constraints and unmodeled dynamics. (1) So as to prevent the violation of the constraints, the barrier Lyapunov function (BLF) method is ingeniously integrated into the entire design process. Considering the existence of unmodeled dynamics, the dynamic signal is introduced to deal with the effect of the unmodeled dynamics on consensus performance. (2) The proposed control architecture depending on the command filter backstepping method is capable of tacking the explosion problem of computation complexity and compensating the errors induced by the command filter. (3) With the finite-time stability theory, the finite-time command filtered backstepping strategy is formulated to accomplish the finite-time consensus controller design. The novel finite-time command filtered backstepping strategy is formulated to resolve the consensus issue for non-strict feedback multiagent systems suffering from the state constraints and unmodeled dynamics. The simulation result further manifests the effectiveness of the proposed adaptive algorithms.