Tracking Control of Wave-Adaptive Modular Unmanned Surface Vehicle Using Time-Delay Zeroing Neural Network
The advancement of artificial intelligence has significantly enhanced the role of unmanned surface vehicles (USVs) in various ocean engineering applications. Designing a controller for USV systems that ensures stability, high precision, and rapid convergence remains a complex challenge in intelligen...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10763501/ |
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| Summary: | The advancement of artificial intelligence has significantly enhanced the role of unmanned surface vehicles (USVs) in various ocean engineering applications. Designing a controller for USV systems that ensures stability, high precision, and rapid convergence remains a complex challenge in intelligent control. In this paper, we begin by developing two high-order backward finite difference formulas (BFDFs) with a second-order truncation error. These formulas are used to approximate the second and third derivatives of smooth functions. Building on these innovative BFDFs, we introduce a second-order tracking controller utilizing a multiple zeroing neural network (ZNN) model with time delay to address the tracking control of the wave-adaptive modular vessel’s vertical motion dynamics (WAMV-VMD) system. The consistency of the proposed tracking controller is established through ordinary differential equation theory. We conduct a series of simulations, and the results demonstrate the effectiveness and advantages of our higher-order tracking controller based on the multiple ZNN model with time delay in managing the tracking control challenges of the WAMV-VMD system. |
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| ISSN: | 2169-3536 |