一阶双曲型偏微分多智能体系统的迭代学习控制

Iterative learning control for first-order hyperbolic partial differential multi-agent systems

针对一类由一阶双曲型偏微分方程构建而成的多智能体系统的迭代学习控制(iterative learning control,ILC)问题,基于系统的拓扑网络结构,利用相邻智能体的信息,构建得到基于一致性的ILC协议,当该ILC律作用于系统时,系统的一致性误差在L2空间有界;当系统的初值偏差为0时,系统的一致性误差能随着迭代学习次数的增加而趋于0。仿真算例验证了算法的有效性。

This paper deals with the iterative learning control(ILC)problem for a class of multi-agent systems with first-order hyperbolic partial differential equations.Based on the framework of network topologies,a consensus-based ILC protocol is proposed by using the nearest neighbor knowledge.When the ILC law is applied to the systems,the consensus errors on L 2 space are bounded,and can converge to zero as the iteration index tends to infinity in the absence of initial errors.Simulation examples illustrate the effectiveness of the proposed method.