Abstract
This paper investigates the saturated kinetic control of autonomous surface vehicles subject to unknown kinetics and limited control torques. The unknown kinetics stems from parametric model uncertainty, unmodelled hydrodynamics, and environmental forces due to wind, waves and ocean currents. By approximating the unknown kinetics using neural networks, a bounded kinetic control law is proposed based on a saturated function, with the main advantage being that the control input is known as a priori. The resulting closed-loop kinetic control system is proved to be input-to-state stable.
The work of Z. Peng was supported in part by the National Natural Science Foundation of China under Grant 51579023, and in part by High Level Talent Innovation and Entrepreneurship Program of Dalian under Grant 2016RQ036, and in part by the Hong Kong Scholars Program under Grant XJ2015009, and in part by the China Post-Doctoral Science Foundation under Grant 2015M570247.
The work of J. Wang was supported in part by the National Natural Science Foundation of China under Grant 61673330, and in part by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grant 14207614.
The work of D. Wang was supported in part by the National Natural Science Foundation of China under Grants 61673081, and in part by the Fundamental Research Funds for the Central Universities under Grant 3132016313, and in part by the National Key Research and Development Program of China under Grant 2016YFC0301500.
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Peng, Z., Wang, J., Wang, D. (2017). Saturated Kinetic Control of Autonomous Surface Vehicles Based on Neural Networks. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10262. Springer, Cham. https://doi.org/10.1007/978-3-319-59081-3_12
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