基于迭代学习的部分非正则多智能体编队控制

针对一类具有任意初始状态的部分非正则多智能体系统,提出一种迭代学习控制算法.该算法将具有固定拓扑结构的多智能体编队控制问题转化为广义上的跟踪问题,即让领导者跟踪给定的期望轨迹,而跟随者要始终保持预定队形对某一智能体进行跟踪,并将该智能体作为自身的领导者.同时,为了使每个智能体在任意初始状态下都能按照期望队形进行编队,对每个智能体的初始状态设计迭代学习律,并从理论上对算法的收敛性进行严格证明,给出算法收敛的充分条件.所提出的算法对于各个智能体在任意初始位置条件下均能实现在有限时间区间内系统的稳定编队.最后,通过仿真算例进一步验证了所提出算法的有效性.

Formation control of partially irregular multi-agent systems with iterative learning

For a class of partially irregular multi-agent systems with arbitrary initial state, an iterative learning control algorithm is proposed. The method transforms formation control of multi-agent systems with fixed topology into a tracking problem in a broader sense, i.e., a leader firstly tracks a given desired trajectory, and then followers one-by-one track a certain agent by maintaining predetermined formation at all times, and this agent is seen as its own leader. At the same time, initial state of each agent is designed by the iterative learning law, in order to make each agent cruise formation in an arbitrary initial state according to expected formation. Moreover, the convergence condition of the algorithm is strictly proved in the theory, as well as the sufficient condition of the convergence condition in the sense of norm is given. The algorithm can realize stable formation of the multi-agent systems in arbitrary initial state in the limited time for all agents. Finally, the effectiveness of the proposed algorithm is further verified by the simulation example.