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Technical Brief

Group Regional Consensus of Networked Lagrangian Systems With Input Disturbances

[+] Author and Article Information
Jun Liu

Shanghai Institute of Applied Mathematics and Mechanics,
Shanghai University,
Shanghai 200072, China;
Department of Mathematics,
Jining University,
Qufu 273155, Shandong, China

Zhonghua Miao

School of Mechatronic Engineering and Automation,
Shanghai University,
Shanghai 200072, China

Jinchen Ji

Faculty of Engineering and IT,
University of Technology Sydney,
PO Box 123, Broadway,
Ultimo 2007, NSW, Australia

Jin Zhou

Shanghai Institute of Applied Mathematics and Mechanics,
Shanghai Key Laboratory of
Mechanics in Energy Engineering,
Shanghai University,
Shanghai 200072, China
e-mail: jzhou@shu.edu.cn

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received March 15, 2016; final manuscript received January 18, 2017; published online June 5, 2017. Assoc. Editor: Dejan Milutinovic.

J. Dyn. Sys., Meas., Control 139(9), 094501 (Jun 05, 2017) (6 pages) Paper No: DS-16-1142; doi: 10.1115/1.4036029 History: Received March 15, 2016; Revised January 18, 2017

Networked multirobot systems under the coordinated control can perform tasks more effectively than a group of individually operating robots. This paper studies the group regional consensus of networked multirobot systems (formulated by second-order Lagrangian dynamics) having input disturbances under directed acyclic topology. An adaptive control protocol is designed to achieve group regional consensus of the networked Lagrangian systems with parametric uncertainties for both leader and leaderless cases. Sufficient conditions are established to guarantee group regional consensus for any prior given desired consensus errors. Compared with the existing work, a distinctive feature of the proposed control algorithm is that the stability analysis indicates the global validity of the obtained consensus results. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.

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Figures

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Fig. 1

The topology of network for leaderless group regional consensus

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Fig. 2

The time response of generalized position errors for leaderless group regional consensus corresponding to ei, i = 1, 2, 3

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Fig. 3

The time response of generalized velocity errors for leaderless group regional consensus corresponding to ei, i = 4, 5, 6

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Fig. 4

The topology of network for group regional consensus with leaders

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Fig. 5

The time response of generalized position errors for group regional consensus with leaders corresponding to Ei, i=1,2,3,4,5,6,7

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Fig. 6

The time response of generalized velocity errors for group regional consensus with leaders corresponding to Ei, i=8,9,10,11,12,13,14

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