This paper investigates a class of output-mask-based adaptive neural network (NN) tracking control for nonlinear stochastic time-delayed multi-agent systems (STMASs) based on a unified event-triggered approach. The output signal relies on an output mapping acted as a mask, defined as a privacy-protection-like method, so that the internal state of one agent cannot be identified by other distrustful eavesdroppers or attackers. Moreover, the construction of a unified event-triggered control scheme retains the advantages of the saturation threshold triggering strategy, incorporates distributed errors, and increases the flexibility of thresholds. Furthermore, for stochastic time-delay multi-agent systems, the initial value limitation of the conventional first-order filter is removed by a first-order Levant differentiator, and a new estimation term in the fuzzy observer is established to solve the nonlinear fault. The unknown function in pure-feedback form is addressed via combining Butterworth low-pass filter and radial basis function neural networks (RBF NNs). Finally, the boundedness of all signals in the closed-loop systems is demonstrated, and the effectiveness of the proposed algorithm is verified by some simulation results.

中文翻译：

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具有统一事件触发方法的随机时滞多智能体系统的基于输出掩模的自适应神经网络控制

本文研究了一类基于输出掩模的自适应神经网络（NN）跟踪控制，用于基于统一事件触发方法的非线性随机时滞多智能体系统（STMAS）。输出信号依赖于充当掩码的输出映射，定义为类似隐私保护的方法，以便一个代理的内部状态无法被其他不信任的窃听者或攻击者识别。此外，统一的事件触发控制方案的构建保留了饱和阈值触发策略的优点，融合了分布式误差，增加了阈值的灵活性。此外，对于随机时滞多智能体系统，通过一阶Levant微分器消除了传统一阶滤波器的初值限制，并在模糊观测器中建立了新的估计项来解决非线性故障。纯反馈形式的未知函数通过结合巴特沃斯低通滤波器和径向基函数神经网络（RBF NN）来解决。最后证明了闭环系统中所有信号的有界性，并通过仿真结果验证了所提算法的有效性。