复杂条件下多智能体系统编队一致性控制

针对噪声信号使多智能体系统难以形成一致的问题,综合运用代数图理论、矩阵论等知识,结合一致性形成条件和现有的一致性协议,提出一种改进的带有噪声的一致性控制算法。该算法能够使系统状态量与控制输入量收敛到一个很小的范围,从而大大弱化噪声对系统的影响。将这种改进算法应用到多智能体编队控制中并进行仿真,仿真结果表明,采用改进的一致性控制算法后,多智能体运动轨迹波动小,轨迹曲线平滑,各运动参数趋于一致,具有良好的编队一致性。

Consensus control for multi-agent system formation under complex conditions

Aimed at at the problem that it is difficult for multi-agent system to form consensus under the impact of the noise signal,an improved consensus control algorithm with noise was proposed by applying algebraic graph and matrix theory comprehensively and considering the formation conditions and existing protocol of consenus. This algorithm enables the state variables and control inputs of the system to converge to a small range and thus can offset the influence of the noise on the system. Simulation of multi-agent formation control has been conducted by using this improred algorithm.Simulation data show that the motion trajectory of multi-agent has a small fluctuation, the track curves are smoother,the motion parameters are consistent and the system has good formation consensus.