Distributed Robust Adaptive Optimization for Nonlinear Multiagent Systems

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.