Composite cooperative synchronization and decentralized learning of multi-robot manipulators with heterogeneous nonlinear uncertain dynamics
This paper studies the problem of composite synchronization and learning of multiple coordinated robot manipulators subject to heterogeneous nonlinear uncertain dynamics under the leader-follower framework. A new two-layer distributed adaptive learning control scheme is proposed, which consists of t...
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| Published in | Journal of the Franklin Institute Vol. 356; no. 10; pp. 5049 - 5072 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elmsford
Elsevier Ltd
01.07.2019
Elsevier Science Ltd |
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| Abstract | This paper studies the problem of composite synchronization and learning of multiple coordinated robot manipulators subject to heterogeneous nonlinear uncertain dynamics under the leader-follower framework. A new two-layer distributed adaptive learning control scheme is proposed, which consists of the first-layer distributed cooperative estimator and the second-layer decentralized deterministic learning controller. The first layer aims to enable each robotic agent to estimate the leader’s information. The second layer is responsible for not only controlling each individual robotic agent to track over desired reference trajectory, but also accurately identifying/learning each robot’s nonlinear uncertain dynamics. Design and implementation of this two-layer distributed controller can be carried out in a fully-distributed manner, which do not require any global information including global connectivity of the communication network. The Lyapunov method is applied to rigorously analyze stability and parameter convergence of the resulting closed-loop system. Numerical simulations on a team of two-degree-of-freedom robot manipulators have been conducted to demonstrate the effectiveness of the proposed results. |
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| AbstractList | This paper studies the problem of composite synchronization and learning of multiple coordinated robot manipulators subject to heterogeneous nonlinear uncertain dynamics under the leader-follower framework. A new two-layer distributed adaptive learning control scheme is proposed, which consists of the first-layer distributed cooperative estimator and the second-layer decentralized deterministic learning controller. The first layer aims to enable each robotic agent to estimate the leader's information. The second layer is responsible for not only controlling each individual robotic agent to track over desired reference trajectory, but also accurately identifying/learning each robot's nonlinear uncertain dynamics. Design and implementation of this two-layer distributed controller can be carried out in a fully-distributed manner, which do not require any global information including global connectivity of the communication network. The Lyapunov method is applied to rigorously analyze stability and parameter convergence of the resulting closed-loop system. Numerical simulations on a team of two-degree-of-freedom robot manipulators have been conducted to demonstrate the effectiveness of the proposed results. |
| Author | Zeng, Wei Yuan, Chengzhi Wang, Cong Stegagno, Paolo Dong, Xiaonan |
| Author_xml | – sequence: 1 givenname: Xiaonan surname: Dong fullname: Dong, Xiaonan email: dong_xn@uri.edu organization: Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA – sequence: 2 givenname: Chengzhi surname: Yuan fullname: Yuan, Chengzhi email: cyuan@uri.edu organization: Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA – sequence: 3 givenname: Paolo surname: Stegagno fullname: Stegagno, Paolo email: pstegagno@uri.edu organization: Department of Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI 02881, USA – sequence: 4 givenname: Wei surname: Zeng fullname: Zeng, Wei email: zw0597@126.com organization: School of Mechanical and Electrical Engineering, Longyan University, Longyan 364012, China – sequence: 5 givenname: Cong surname: Wang fullname: Wang, Cong email: wangcong@scut.edu.cn organization: School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China |
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| SubjectTerms | Adaptive control Adaptive learning Computer simulation Controllers Feedback control Learning Manipulation Manipulators Multiple robots Nonlinear dynamics Nonlinear systems Robot arms Robots Stability analysis Synchronism |
| Title | Composite cooperative synchronization and decentralized learning of multi-robot manipulators with heterogeneous nonlinear uncertain dynamics |
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