Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

doi:10.1088/1674-1056/ac21c1

中国物理B ›› 2022, Vol. 31 ›› Issue (2): 20203-020203.doi: 10.1088/1674-1056/ac21c1

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

Ximing Wang(王希铭), Jinsheng Sun(孙金生)^†, Zhitao Li(李志韬), and Zixing Wu(吴梓杏)

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

收稿日期:2021-04-01 修回日期:2021-08-07 接受日期:2021-08-27 出版日期:2022-01-13 发布日期:2022-01-13
通讯作者: Jinsheng Sun E-mail:jssun67@163.com

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

Ximing Wang(王希铭), Jinsheng Sun(孙金生)^†, Zhitao Li(李志韬), and Zixing Wu(吴梓杏)

School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China

Received:2021-04-01 Revised:2021-08-07 Accepted:2021-08-27 Online:2022-01-13 Published:2022-01-13
Contact: Jinsheng Sun E-mail:jssun67@163.com

摘要/Abstract

摘要： This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer. To compensate for external disturbances, a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information. With the filtered regressor method, the algorithm has the advantage of eliminating the need for acceleration information, thus reducing the sensor requirements in applications. Using the information obtained from the filtered regressor, a batch of stored data is used to design an adaptive disturbance observer, ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values. The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior, including virtual leader tracking, inter-distance keeping, and collision avoidance. Numerical simulations verify the effectiveness of the algorithm proposed in the present study.

关键词: flocking control, multi-agent systems, adaptive control, disturbance rejection

Abstract: This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer. To compensate for external disturbances, a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information. With the filtered regressor method, the algorithm has the advantage of eliminating the need for acceleration information, thus reducing the sensor requirements in applications. Using the information obtained from the filtered regressor, a batch of stored data is used to design an adaptive disturbance observer, ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values. The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior, including virtual leader tracking, inter-distance keeping, and collision avoidance. Numerical simulations verify the effectiveness of the algorithm proposed in the present study.

Key words: flocking control, multi-agent systems, adaptive control, disturbance rejection

中图分类号: (Control theory)

02.30.Yy

Ximing Wang(王希铭), Jinsheng Sun(孙金生), Zhitao Li(李志韬), and Zixing Wu(吴梓杏). Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances[J]. 中国物理B, 2022, 31(2): 20203-020203.

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Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

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