Neural Network Identification-Based Model Predictive Heading Control for Wave Gliders
This paper deals with the neural network identification-based model predictive heading control problem in a wave glider. First, based on a kinematic model of the wave glider subjected to external disturbance and system uncertainty, a state space model of the wave glider is established. Then, a neura...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/12/12/2279 |
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| Summary: | This paper deals with the neural network identification-based model predictive heading control problem in a wave glider. First, based on a kinematic model of the wave glider subjected to external disturbance and system uncertainty, a state space model of the wave glider is established. Then, a neural network identification-based model predictive heading controller (NNI-MPHC) is designed for the wave glider. The heading controller mainly includes three components: a model predictive controller, a neural network-based model identifier, and a linear reduced-order extended state observer. Third, a design algorithm of the NNI-MPHC is presented. The algorithm is demonstrated through simulation, where the results show the following: (i) The designed NNI-MPHC is remarkably capable of guaranteeing the tracing effects of the wave glider. (ii) Comparing the NNI-MPHC and existing heading controllers, the former is better than the latter in terms of tracking accuracy and rapidity and robustness to model uncertainty and/or external disturbances. |
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| ISSN: | 2077-1312 |