未知异构非线性多智能体系统的无模型自适应编队控制  

Model-free adaptive formation control for unknown heterogeneous nonlinear multi-agent systems

作　　者：金尚泰[1] 李澈 任叶 侯忠生 JIN Shang-tai;LI Che;REN Ye;HOU Zhong-sheng(School of Electronic and Information Engineering,Beijing Jiaotong University,Beijing 100044,China;School of Automation,Qingdao University,Qingdao 266071,China)

机构地区：[1]北京交通大学电子信息工程学院,北京100044 [2]青岛大学自动化学院,山东青岛266071

出　　处：《控制与决策》2020年第6期1519-1524,共6页Control and Decision

基　　金：国家自然科学基金项目(61573054,61433002,61833001);北京市自然科学基金项目(4182068).

摘　　要：研究一类未知异构非线性多智能体的编队控制问题.首先,利用全格式动态线性化(full form dynamic linearization, FFDL)方法将未知非线性智能体转化为含有时变参数的数据模型,并给出时变参数的估计方法;然后,基于该数据模型设计一种分布式无模型自适应多智能体编队控制方案;最后,为验证所提出的无模型自适应编队控制方案的有效性,利用3台NAO机器人开发基于Python的多智能体编队控制实验平台.实验比较结果表明,通过所提出的控制方案可使3台机器人仅利用局部信息就能有效完成编队控制任务,控制性能优于基于PID的编队控制方法.The formation control problem is considered for a class of unknown heterogeneous nonlinear multi-agent systems. Firstly, the unknown nonlinear agent is transformed into a data model with time-varying parameters by using the full form dynamic linearization(FFDL) method. The estimation method of time-varying parameters is given. Then, a distributed model-free adaptive multi-agent formation control scheme is designed based on the FFDL data model. Finally,the effectiveness of the proposed model-free adaptive formation control scheme is verified. A multi-agent formation control experimental platform based on Python is developed by using three Nao robots. The experimental comparison results show that, using the proposed control scheme, the three robots can effectively complete the formation control task only by using local information. The control performance is better than the PID based formation control method.

关 键 词：多智能体系统 无模型自适应控制 图论法 编队控制 NAO机器人 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]