Abstract
In this paper, an intelligent fuzzy kinetic control scheme based on online identification is developed for an under-actuated autonomous surface vehicle in the presence of unknown uncertainties and disturbances from ocean environment. An adaptive fuzzy system is used to approximate the unknown dynamics in real time by using recorded input and output data of the vessel. To improve the learning performance, the parameters of the fuzzy system are updated based on a stochastic gradient descent approach and a predictor design. With the estimated dynamics from the fuzzy system, a robust kinetic controller is designed without any off-line learning. The proposed intelligent fuzzy control method can be applied at the kinetic level of various control scenarios, such as target tracking, path following and trajectory tracking.
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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Jiang, Y., Liu, L., Peng, Z., Wang, D., Gu, N., Gao, S. (2019). Intelligent Fuzzy Kinetic Control for an Under-Actuated Autonomous Surface Vehicle via Stochastic Gradient Descent. 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_10
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