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Model-based control methods are commonly employed to address this problem, but they suffer from issues related to the time-variability of parameters and the inaccuracy of mathematical models. To improve these, a meta-learning and self-adaptation hybrid approach is proposed in this paper to enable an underwater robot to adapt to ocean currents. Instead of using a traditional complex mathematical model, a deep neural network (DNN) serving as the basis function is trained to learn a high-order hydrodynamic model offline; then, a set of linear coefficients is adjusted dynamically by an adaptive law online. By conjoining these two strategies for real-time thrust compensation, the proposed method leverages the potent representational capacity of DNN along with the rapid response of adaptive control. This combination achieves a significant enhancement in tracking performance compared to alternative controllers, as observed in simulations. These findings substantiate that the AUV can adeptly adapt to new speeds of ocean currents.<\/jats:p>","DOI":"10.3390\/s23146417","type":"journal-article","created":{"date-parts":[[2023,7,17]],"date-time":"2023-07-17T01:06:36Z","timestamp":1689555996000},"page":"6417","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Real-Time Ocean Current Compensation for AUV Trajectory Tracking Control Using a Meta-Learning and Self-Adaptation Hybrid Approach"],"prefix":"10.3390","volume":"23","author":[{"given":"Yiqiang","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Jiaxing","family":"Che","sequence":"additional","affiliation":[{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen 518110, China"}]},{"given":"Yijun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China"}]},{"given":"Jiankuo","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}]},{"given":"Junhong","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"},{"name":"Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China (UESTC), Shenzhen 518110, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhou, J., Si, Y., and Chen, Y. 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