Distributed Optimization for Uncertain High-Order Nonlinear Multiagent Systems via Dynamic Gain Approach
针对不确定高阶非线性多智能体系统，提出分布式输出优化方法，利用动态增益处理未知最优解，设计分布式协调器和参考跟踪控制器，使输出跟踪误差在有限时间内任意小。
In this article, we investigate the distributed output optimization for general uncertain high-order nonlinear multiagent systems (MASs), where nonlinear functions are constrained by a linear growth condition. The dynamic gain approach is utilized to cope with the influence of the unknown optimal solution. First, the distributed optimal coordinators (DOCs) with an adjustable parameter are constructed to steer the generated signals converging to the optimal solution. By developing the iterative design strategy, the dynamic reference-tracking controllers are then designed so that the output of each agent follows the generated value of coordinators, respectively. It is proved that all states are globally bounded, as well as the tracking error between the outputs and optimal solution can be bounded in a finite time by an arbitrarily small constant. Simulation studies demonstrate the validness of the main idea.