Soft control on collective behavior of a group of autonomous agents by a shill agent

被引:72
作者
Han J. [1 ,3 ]
Li M. [2 ]
Guo L. [3 ]
机构
[1] Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
[2] Institute of Theoretical Physics, Chinese Academy of Sciences
[3] Academy of Mathematics and Systems Science, Chinese Academy of Sciences
基金
中国国家自然科学基金;
关键词
Boid model; Collective behavior; Multi-agent system; Shill agent; Soft control;
D O I
10.1007/s11424-006-0054-z
中图分类号
学科分类号
摘要
This paper asks a new question: how can we control the collective behavior of self-organized multi-agent systems? We try to answer the question by proposing a new notion called 'Soft Control', which keeps the local rule of the existing agents in the system. We show the feasibility of soft control by a case study. Consider the simple but typical distributed multi-agent model proposed by Vicsek et al. for flocking of birds: each agent moves with the same speed but with different headings which are updated using a local rule based on the average of its own heading and the headings of its neighbors. Most studies of this model are about the self-organized collective behavior, such as synchronization of headings. We want to intervene in the collective behavior (headings) of the group by soft control. A specified method is to add a special agent, called a 'Shill', which can be controlled by us but is treated as an ordinary agent by other agents. We construct a control law for the shill so that it can synchronize the whole group to an objective heading. This control law is proved to be effective analytically and numerically. Note that soft control} is different from the approach of distributed control}. It is a natural way to intervene in the distributed systems. It may bring out many interesting issues and challenges on the control of complex systems. © Springer Science + Business Media, Inc. 2006.
引用
收藏
页码:54 / 62
页数:8
相关论文
共 14 条
[1]  
Helbing D., Farkas I., Vicsek T., Simulating dynamical features of escape panic, Nature, 407, pp. 487-490, (2000)
[2]  
Axtell R.L., Chakravarty S., Radicals, Revolutionaries and Reactionaries in A Multi-agent Model of Class Norms
[3]  
Anderson P.W., More is different, Science, 177, pp. 393-396, (1972)
[4]  
Bonabeau E., Dorigo M., Theraulaz G., Swarm Intelligence: from Natural to Artificial Systems, (1999)
[5]  
Han J., Guo L., Li M., Guiding a group of locally interacting autonomous mobile agents, The 2nd Chinese-Swedish Conference on Control, (2004)
[6]  
Reynolds C., Flocks, birds, and schools: A distributed behavioral model, Computer Graphics, 21, pp. 25-34, (1987)
[7]  
Vicsek T., Czirok A., Jacob E.B., Cohen I., Schochet O., Novel type of phase transitions in a system of self-driven particles, Physical Review Letters, 75, pp. 1226-1229, (1995)
[8]  
Desai J., Otrowski J., Kumar V., Modeling and control of formations of nonholonomic robots, IEEE Trans. on Robotics and Automation, 17, 6, pp. 905-908, (2001)
[9]  
Wang X.F., Chen G., Pinning control of scale-free dynamical networks, Physica, 310, A, pp. 521-531, (2002)
[10]  
Jadbabaie A., Lin J., Morse A.S., Coordination of groups of mobile autonomous agents using nearest neighbor rules, IEEE Trans. on Automatic Control, 48, pp. 988-1001, (2003)