This paper investigates the consensus tracking problem with predefined performances requirements for a class of unknown nonlinear multi-agent systems with hysteresis quantizer and external disturbances under a directed graph topology. Neural network observers are designed to estimate unmeasurable states and the the consensus tracking problem with performance requirements is transformed to a stabilization problem by prescribed performance error transformation schemes. The novel consensus protocol can be applied to a more general class of nonlinear multi-agent systems since the Lipschitz condition is avoided and state information is not required. It is strictly proved that all signals in the closed-loop systems are cooperatively uniformly ultimately bounded and both the transient and steady performances of the consensus tracking satisfy prescribed performance requirements. Finally, two numerical examples are presented to validate the effectiveness of the proposed strategy.

本文研究了有向图拓扑下具有滞后量化器和外部干扰的一类未知非线性多智能体系统在预定义性能要求下的共识跟踪问题。将神经网络观察者设计为估计不可测量的状态，并通过规定的性能误差转换方案将具有性能要求的共识跟踪问题转换为稳定问题。由于避免了Lipschitz条件并且不需要状态信息，因此可以将新颖的共识协议应用于更通用的非线性多主体系统类别。严格证明，闭环系统中的所有信号最终都协同一致地有界，并且共识跟踪的瞬态和稳态性能均满足规定的性能要求。最后，通过两个数值例子验证了所提策略的有效性。