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Abstract
This article addresses the time-varying formation-containment control problem for networked second-order systems with unknown nonlinear dynamics. The communication topology among agents is switching and directed. To simultaneously achieve formation configuration for leaders and accomplish containment behavior for followers, adaptive distributed formation and containment control schemes are developed. In the control framework design, the neural-network control technique is utilized to approximate the unknown nonlinear dynamics. The update frequency and computation resources of the controllers are reduced by dynamic triggering mechanisms. It is proved that no agent exhibits the Zeno behavior on the time-varying asymmetric communication topology. Moreover, no prior knowledge of global information is needed in controller and triggering mechanism implementations. All system parameters can be easily and flexibly chosen. Finally, numerical examples are presented to illustrate the effectiveness of the proposed control schemes. © 2023 IEEE.
Original language | English |
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Pages (from-to) | 951-963 |
Journal | IEEE Transactions on Control of Network Systems |
Volume | 11 |
Issue number | 2 |
Online published | 6 Nov 2023 |
DOIs | |
Publication status | Published - Jun 2024 |
Funding
This work was supported by the National Natural Science Foundation of China under Grants T2121002 and 62173006, the Hong Kong Research Grants Council under Grant CityU 11206320, and the China Postdoctoral Science Foundation under Grants 2022TQ0029 and 2022M720435.
Research Keywords
- adaptive control
- Control systems
- dynamic event-triggered control
- FCC
- Formation-containment control
- Nonlinear dynamical systems
- second-order nonlinear system
- Shape
- Switches
- switching directed graph
- Symmetric matrices
- Topology
Fingerprint
Dive into the research topics of 'Adaptive Distributed Formation-Containment Control on Switching Directed Networks: A Dynamic Triggering Framework'. Together they form a unique fingerprint.Projects
- 1 Finished
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GRF: Analyzing the Robustness of Network Controllability against Malicious Attacks
CHEN, G. (Principal Investigator / Project Coordinator) & TANG, K. S. W. (Co-Investigator)
1/01/21 → 28/05/24
Project: Research