Abstract
In this paper, a surge speed tracking problem for an unmanned surface vehicle (USV) driven by a direct current (DC) motor is investigated. The surge velocity tracking controller is developed based on a modular neural dynamic surface control (MNDSC) design method. Specifically, two neural-network-based identifiers are designed to identify the uncertainties existing in surge dynamics, propeller and DC motor. Then, a robust adaptive surge speed controller based on a dynamic surface control design method is designed by using the recovered unknown information from identifiers. The proposed surge velocity tracking controller is able to track any time-varying bounded velocity profiles. The stability of the closed-loop system is established based on cascade theory and input-to-state stability (ISS) theory. The effectiveness of the proposed surge speed controller for the USV is illustrated via simulations.
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 Training 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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Meng, C., Liu, L., Peng, Z., Wang, D., Wang, H., Sun, G. (2019). Neural-Network-Based Modular Dynamic Surface Control for Surge Speed Tracking of an Unmanned Surface Vehicle Driven by a DC Motor. 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_8
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