Event-Triggered Neuro-Adaptive Cooperative Control for Nonlinear Multi-Agent Systems with Full State Constraints and Prescribed Performance

This paper proposes an event-triggered adaptive control algorithm, considering the full state constraints and prescribed performance, to address the cooperative tracking problem of a class of non-strict feedback nonlinear multi-agent systems. First, a performance function is designed to allow the tracking error to be converge within a specified range within a preset time. Then, the Barrier Lyapunov function is introduced into the backstepping method, so that all states meet the constraints. This is combined with the dynamic surface technology to solve the "calculation explosion" problem caused by the traditional backstepping method. The radial basis function neural networks are used to deal with the unknown nonlinear function. Finally, based on the Lyapunov stability theory, it is proved that all the signals in the system are semi -globally uniform and ultimately bounded, and the tracking error converges to the bounded neighborhood of zero and satisfies the prescribed performance. At the end, the effectiveness of the proposed control algorithm is verified through simulation results.