一阶多智能体受扰系统的自抗扰分布式优化算法

研究一类具有未知外部干扰的一阶多智能体系统的分布式优化问题.在分布式优化任务中,每个智能体只被容许利用自己的局部目标函数和邻居的状态信息,设计一个分布式优化算法,使全局目标函数取得最小值,其中全局目标函数是所有局部目标函数之和.针对该问题,首先提出由扩张状态观测器和优化算法组成的自抗扰分布式优化算法.其次,在Lyapu...

Active disturbance rejection distributed optimization algorithm for first-order multi-agent disturbance systems

The paper investigates a distributed optimization algorithm for a class of first-order multi-agent systems with unknown external disturbance. In the distributed optimization task, each agent is only allowed to use its own local cost function and the state information of its neighbors to design a distributed optimization algorithm, so that the global cost function which is the sum of all local cost functions obtains the minimum value. To solve this problem, an active disturbance rejection control distributed optimization algorithm which consists of an extended state observer and an optimization algorithm is proposed. Then, based on the Lyapunov stability, a new method is developed to prove the convergence and stability of the closed-loop system rigidly. When the external disturbance is constant, the designed method can make the states of all agents exponentially converge to the minimum of the global cost function. When the external disturbance is bounded, by adjusting the gain parameter of the extended state observer, the designed method can make the states of all agents converge to an arbitrarily small neighbourhood of the global cost function''s minimum value. Finally, the simulation results show the effectiveness of the proposed algorithm.