Learning-Aided Adaptive Robust Control for Spiral Trajectory Tracking of an Underactuated AUV in Net-Cage Environments

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dc.contributor.authorZhiming Zhu
dc.contributor.authorHuang Dazhi
dc.contributor.authorYang Feifei
dc.contributor.authorHe Hongkun
dc.contributor.authorLiang Fuyuan
dc.contributor.authorVoitasyk Andrii
dc.date.accessioned2025-10-02T06:38:04Z
dc.date.issued2025-09
dc.descriptionLearning-Aided Adaptive Robust Control for Spiral Trajectory Tracking of an Underactuated AUV in Net-Cage Environments / Z. Zhu, D. Huang, F. Yang, H. He, F. Liang, A. Voitasyk // Journal article research article Published by MDPI AG in Applied Sciences. – 2025. – Vol. 15 (19). – P. 10477.
dc.description.abstract1High-precision spiral trajectory tracking for aquaculture net-cage inspection is hindered by uncertain hydrodynamics, strong coupling, and time-varying disturbances acting on an underactuated autonomous underwater vehicle. This paper adapts and validates a model– data-driven learning-aided adaptive robust control strategy for the specific challenge of high-precision spiral trajectory tracking for aquaculture net-cage inspection. At the kinematic level, a serial iterative learning feedforward compensator is combined with a lineof-sight guidance law to form a feedforward-compensated guidance scheme that exploits task repeatability and reduces systematic tracking bias. At the dynamic level, an integrated adaptive robust controller employs projection-based, rate-limited recursive least-squares identification of hydrodynamic parameters, along with a composite feedback law that combines linear error feedback, a nonlinear robust term, and fast dynamic compensation to suppress lumped uncertainties arising from estimation error and external disturbances. A Lyapunov-based analysis establishes uniform ultimate boundedness of all closed-loop error signals. Simulations that emulate net-cage inspection show faster convergence, higher tracking accuracy, and stronger robustness than classical adaptive robust control and other baselines while maintaining bounded control effort. The results indicate a practical and effective route to improving the precision and reliability of autonomous net-cage inspection.
dc.description.provenanceSubmitted by Войтасик Андрій Миколайович (andrii.voitasyk@nuos.edu.ua) on 2025-09-29T14:15:28Z workflow start=Step: reviewstep - action:claimaction No. of bitstreams: 1 applsci-15-10477-with-cover_Voitasyk.pdf: 1068742 bytes, checksum: a7ea0c4de594a1156b13ab862507518b (MD5)en
dc.description.provenanceStep: reviewstep - action:reviewaction Approved for entry into archive by Диндеренко Катерина (kateryna.dynderenko@nuos.edu.ua) on 2025-10-02T06:33:12Z (GMT)en
dc.description.provenanceStep: editstep - action:editaction Approved for entry into archive by Диндеренко Катерина (kateryna.dynderenko@nuos.edu.ua) on 2025-10-02T06:37:47Z (GMT)en
dc.description.provenanceStep: finaleditstep - action:finaleditaction Approved for entry into archive by Диндеренко Катерина (kateryna.dynderenko@nuos.edu.ua) on 2025-10-02T06:38:04Z (GMT)en
dc.description.provenanceMade available in DSpace on 2025-10-02T06:38:04Z (GMT). No. of bitstreams: 1 Zhiming_ Huang_ Yang_ He_ Liang_ Voitasyk.pdf: 1068742 bytes, checksum: a7ea0c4de594a1156b13ab862507518b (MD5) Previous issue date: 2025-09en
dc.identifier.govdochttps://doi.org/10.3390/app151910477
dc.identifier.issn2076-3417 (Online)
dc.identifier.urihttps://eir.nuos.edu.ua/handle/123456789/11286
dc.language.isoen
dc.subjectunderactuated AUVs
dc.subjectnet-cage inspection
dc.subjectspiral trajectory tracking
dc.subjectlearningaided adaptive robust control
dc.subjectLARC
dc.subjectdisturbance rejection
dc.titleLearning-Aided Adaptive Robust Control for Spiral Trajectory Tracking of an Underactuated AUV in Net-Cage Environments
dc.typeArticle

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