输出饱和多智能体系统的迭代学习趋同跟踪控制

针对带有输出饱和的多智能体系统有限时间趋同跟踪控制问题,提出了一种分布式迭代学习控制算法.首先假设多智能体系统具有固定拓扑结构,且仅有部分智能体可获取到期望轨迹信息.基于输出约束条件构造一致性跟踪误差,在此基础上设计了P型迭代学习控制率.然后采用压缩映射方法给出了一个算法收敛的充分条件,并在理论上证明了跟踪误差的收敛性.最后,将理论结果推广至具有随机切换拓扑结构的多智能体系统中.仿真结果验证了所提出算法的有效性.

Iterative learning consensus tracking control for a class of multi-agent systems with output saturation

In this paper, a distributed iterative learning control algorithm is proposed to the consensus tracking control problem of multi-agent systems with output saturation. First, it is assumed that the considered multi-agent system has a fixed communication topology and only a part of agents can obtain the desired trajectory information. The P-type iterative learning control law is developed from the consensus tracking error that constructed by the constraint output. Then, a sufficient condition of the algorithm is given by using the approach of contraction mapping, and the theoretical convergence analysis of the tracking error is also provided. Finally, the theoretical results are extended into multi-agent systems with randomly switching topology. Simulation results further validate the effectiveness of the proposed algorithm.