初始误差修正的多智能体一致性迭代学习控制

研究了重复运行的分布式多智能体系统在有限时间内的一致性问题。针对具有固定拓扑结构的多智能体系统，在期望轨迹对应的初始状态未知，且系统存在干扰的情况下，引入虚拟领导者技术，提出了一种同时对各智能体的输入和初始状态误差进行迭代修正的分布式学习控制算法。收敛性分析表明，该算法能够消除由于各智能体初始状态和期望轨迹对应的初始状态不同而引起的各智能体输出不能完全跟踪期望轨迹的状况，实现系统在有限时间内的完全跟踪；仿真结果也证明了算法的有效性。

Iterative learning control of multi-agent consensus with initial error correction

This paper considers a finite-time consensus problem for a distributed multi-agent system executing a given repetitive task. The virtual leader technology is introduced for the system with fixed topology. Under the condition of unknown initial states corresponding to the desired trajectory, a distributed learning control algorithm combined with initial state correction of every agent is proposed for the system with interference. The convergence analysis shows that the proposed algorithm can eliminate the tracking errors caused by the different initial states between every agent and the desired trajectory, and the perfect tracking control within the finite-time is available. Simulate results also prove the effectiveness of the proposed algorithm.