Distributed Asymptotic Tracking for Higher-Order Multi-Agent Systems With Unknown Control Directions Under Directed Graphs
This paper addresses the distributed tracking problem for higher-order nonlinear multi-agent systems characterized by unknown control directions and uncertain dynamics under directed graphs. A novel adaptive control algorithm is developed using Nussbaum-type functions, nonlinear transformations, and...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10906585/ |
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| Summary: | This paper addresses the distributed tracking problem for higher-order nonlinear multi-agent systems characterized by unknown control directions and uncertain dynamics under directed graphs. A novel adaptive control algorithm is developed using Nussbaum-type functions, nonlinear transformations, and compensating functions to ensure the asymptotic convergence of all agents to the leader while maintaining the boundedness of all closed-loop signals. Unlike previous results, the presented approach does not rely on function approximators, guarantees asymptotic convergence rather than bounded residual errors, and applies to general directed graphs containing a spanning tree. Theoretical analysis, supported by Lyapunov stability theory, establishes the effectiveness of the proposed controller in handling unknown control directions and system uncertainties. Comparative simulation studies involving a network of single-link robots demonstrate the superior performance of the proposed method in achieving precise distributed tracking. The results show improved convergence accuracy and computational efficiency over existing techniques, making this approach a promising solution for distributed control applications in multi-agent systems. |
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| ISSN: | 2169-3536 |