Adaptive output-constrained finite-time formation control for multiple unmanned surface vessels with directed communication topology
This paper tackles the intricate task of leader-following formation control for multiple unmanned surface vessels (USVs) operating over a directed communication network. Our investigation takes into meticulous consideration several critical variables, including prescribed output constraints, the pre...
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| Published in: | Ocean engineering Vol. 292; p. 116552 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
15.01.2024
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| Subjects: | |
| ISSN: | 0029-8018, 1873-5258 |
| Online Access: | Get full text |
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| Summary: | This paper tackles the intricate task of leader-following formation control for multiple unmanned surface vessels (USVs) operating over a directed communication network. Our investigation takes into meticulous consideration several critical variables, including prescribed output constraints, the presence of enigmatic dynamics, and the pervasive influence of external disturbances on each individual USV. Our methodology commences with the introduction of an ingenious output transformation technique, which adeptly converts constrained output variables into their unconstrained counterparts. Subsequently, we craft a novel distributed tracking error metric, artfully harnessing the potential of the transformed output. We build an adaptive, non-singular finite-time formation controller, firmly rooted in the backstepping framework. This controller seamlessly integrates vital components such as virtual velocity control, command filters, neural networks, and parametric adaptive strategies, which is underpinned by our meticulous attention to maintaining C1 continuity within our proposed virtual control law. Furthermore, we introduce the concept of minimum parameter learning, a strategic inclusion aimed at streamlining the computational demands of our controller. Significantly, any potential output overshoot resulting from minimum parameter learning is adeptly managed through our innovative output-constrained control strategy. To substantiate the effectiveness and robustness of our approach, we provide an exhaustive stability analysis, which offers conclusive evidence of our controller’s performance under a diverse array of conditions.
•A non-singular finite-time formation controller for multiple USVs is proposed.•A novel output-constrained control is proposed in a distributed framework.•The designed neural adaptive control law has low computational cost.•The multiple USVs considered with directed communication has good universality. |
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| ISSN: | 0029-8018 1873-5258 |
| DOI: | 10.1016/j.oceaneng.2023.116552 |