有向图中网络Euler-Lagrange系统无需相对速度信息的群一致性

Group Consensus for Networked Euler-Lagrangian Systems Under a Directed Graph Without Relative Velocity Information

在非对称有向图中,研究网络Euler-Lagrange系统的群一致性问题.每组内的智能体均为合作关系,而组间智能体则可以为合作关系或竞争关系.为了实现群一致性,假设组与组之间是无环连接的且系统有向图满足入度平衡条件.考虑到智能体间相对速度信息难以精确测量的实际情形,设计无需相对速度信息的分布式自适应控制算法,实现网络Euler-Lagrange系统的群一致性.最后通过仿真分析验证所设计算法的有效性.

In this paper, we study group consensus for networked Euler-Lagrangian systems under a general nonsym- metric directed graph. The relationships between interactive agents in same group are cooperative while the relationships between interactive agents among different groups can be either cooperative or competitive. To achieve group consensus, we assume that the interaction topology among groups is acyclic and the associated directed graph satisfies the in-degree balance condition. Considering the fact that the relative velocity information among neighboring agents is difficult to obtain, we propose a distributed adaptive control algorithm for each agent without relative velocity information to achieve group consensus for networked Euler-Lagrangian systems. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control algorithm.