This paper investigates the distributed adaptive optimization problem for nonlinear multiagent systems with external disturbances. The main goal is to optimize a global objective function by utilizing local and neighboring information while rejecting the external disturbance signals. Different from the existing results, the weight-balanced directed graph is considered and, by introducing the adaptive technique, the local objective functions are allowed only to be differentiable with locally Lipschitz gradients. Moreover, without requiring the system nonlinear functions to be globally Lipschitz, the global asymptotic convergence is obtained if the global objective function is strongly convex. Finally, simulation results are provided to verify the validity of the proposed algorithm.

中文翻译：

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非线性多智能体系统的分布式鲁棒自适应优化

本文研究了具有外部干扰的非线性多智能体系统的分布式自适应优化问题。主要目标是通过利用局部和邻近信息同时拒绝外部干扰信号来优化全局目标函数。与现有结果不同，考虑了权重平衡的有向图，并通过引入自适应技术，只允许局部目标函数与局部 Lipschitz 梯度可微。而且，不需要系统非线性函数是全局Lipschitz，如果全局目标函数是强凸的，就可以得到全局渐近收敛。最后，通过仿真结果验证了所提算法的有效性。