Abstract
In this paper, a robust identifier is developed for unmanned surface vehicles (USVs) subject to uncertain dynamics. The uncertain dynamics comes from parametric model uncertainty and external ocean disturbance. The identifier for USV is designed based on Robust Integral Sign of the Error (RISE) and neural networks. With the proposed identifier, asymptotic stability of the estimation errors can be proven in the presence of parametric model uncertainties and external ocean disturbances. The proposed method can be used in a variety of practical settings such as trajectory tracking and formation control of marine vehicles for achieving better performance.
L. Liu, Z. Peng, D. Wang—This work was supported in part by the National Natural Science Foundation of China under Grants 51579023, 61673081, the Innovative Talents in Universities of Liaoning Province under Grant LR2017014, High Level Talent Innovation and Entrepreneurship Program of Dalian under Grant 2016RQ036, China Postdoctoral Science Foundation 2019M650086, the National Key Research and Development Program of China under Grant 2016YFC0301500, the Traning Program for High-level Technical Talent in Transportation Industry under Grant 2018-030, the Fundamental Research Funds for the Central Universities under Grant 3132019101,3132019013, and Outstanding Youth Support Program of Dalian.
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Gao, S., Liu, L., Peng, Z., Wang, D., Gu, N., Jiang, Y. (2019). An Asymptotically Stable Identifier Design for Unmanned Surface Vehicles Based on Neural Networks and Robust Integral Sign of the Error. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_6
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