具有指定性能和全状态约束的多智能体系统事件触发控制

针对一类非严格反馈的非线性多智能体系统一致性跟踪问题，在考虑全状态约束和指定性能的基础上提出了一种事件触发自适应控制算法.首先，通过设计性能函数，使跟踪误差在规定时间内收敛于指定范围.然后，在反步法中引入Barrier Lyapunov函数使所有状态满足约束条件，结合动态面技术解决传统反步法产生的"计算爆炸"问题，并利用径向基函数神经网络（Radial basis function neural networks，RBF NNs）处理系统中的未知非线性函数.最后基于Lyapunov稳定性理论证明系统中所有信号都是半全局一致最终有界的，跟踪误差收敛于原点的有界邻域内且满足指定性能.仿真结果验证了该控制算法的有效性.

Event-Triggered Control for Multi-Agent Systems With Prescribed Performance and Full State Constraints

In this paper, an event-triggered adaptive control algorithm is proposed to address the consensus tracking problem of non-strict feedback nonlinear multi-agent systems with prescribed performance and full state constraints. With the aid of performance function, tracking error converges to a specified range within a specified time. By introducing the Barrier Lyapunov function into the backstepping approach, the full states of the systems satisfy the constraints. And the dynamic surface technique is employed to dispose the problem of "explosion of complexity". Moreover, the radial basis function neural networks (RBF NNs) are utilized to approximate unknown nonlinear functions in the systems. According to the Lyapunov stability theory, all signals are semi-globally uniformly ultimately bounded, and the tracking error converges to a bounded neighborhood of the origin with prescribed performance. Finally, the effectiveness of the proposed control algorithm is verified by simulation results.