由多个载体组成的多智能体系统对复杂环境具有更高的适应性，能够完成传统单个载体无法完成的任务。针对多智能体编队集结与队形移动跟踪问题，提出了一种改进的多智能体编队协同控制新算法。首先，以拒止环境下跟随智能体仅能通过光学传感器测量相对方位信息为任务背景，针对"领导者——第一跟随者"结构的多智能体编队，提出了基于相对方位信息与单间距测量的控制器，使得第一跟随者智能体可以追随移动的领导者智能体，并且可以通过改变与领导者智能体的间距对编队整体队形进行缩放控制。其次，提出一种了改进的分布式控制律，使得其他跟随者智能体可以仅通过两个相对方位信息完成编队飞行。然后，根据Lyapunov第二方法，构建了系统的能量函数，验证了所提出算法的稳定性。最后，通过数值仿真实验对所提算法进行了验证。仿真结果表明，基于该控制律多智能体系统能够完成编队集结、队形缩放和编队飞行的任务。
The multi-agent system composed of multiple carriers is more adaptable to complex environments and can accomplish tasks that traditional single carriers cannot. For the problem of multi-agent formation assembly and formation movement tracking, an improved new algorithm for multi-agent formation cooperative control is proposed. First, based on the task background that the follower agent can only measure relative bearing information through optical sensors in the denial environment, a control law based on relative bearing information and single distance measurement is proposed for the multi-agent formation of the "leader-first follower" structure. Then, the first follower agent can follow the moving leader agent, and the overall team shape of the formation can be zoomed and controlled by changing the distance from the first follower agent to the leader agent. Second, an improved distributed control law is proposed to enable the rest of the follower agents to complete formation flying through only two relative position information. Then, according to the second method of Lyapunov, the energy function of the system is constructed, and the stability of the proposed algorithm is verified. Finally, the effectiveness of the proposed algorithm is confirmed by numerical simulation experiments, which show that the multi-agent system based on the control law can complete the tasks of formation assembly, formation zooming and formation flying.
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