论文信息 - Predicting the Trajectory Tracking Control of Unmanned Surface Vehicle Based on Deep Learning - 字舞流文

Predicting the Trajectory Tracking Control of Unmanned Surface Vehicle Based on Deep Learning

Wenli Sun | Xu Gao

[1] Signe Moe,et al. Machine Learning in Control Systems: An Overview of the State of the Art , 2018, SGAI Conf..

[2] Satyandra K. Gupta,et al. Target following with motion prediction for unmanned surface vehicle operating in cluttered environments , 2014, Auton. Robots.

[3] Michael Novitzky,et al. Learning Autonomous Marine Behaviors in MOOS-IvP , 2018, OCEANS 2018 MTS/IEEE Charleston.

[4] Yuanyuan Shi,et al. Optimal Control Via Neural Networks: A Convex Approach , 2018, ICLR.

[5] Yan Wang,et al. Unmanned Surface Vehicle Course Tracking Control Based on Neural Network and Deep Deterministic Policy Gradient Algorithm , 2018, 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO).

[6] Steven M. LaValle,et al. Planning algorithms , 2006 .

[7] Yongsheng Zhao,et al. Adaptive RBF neural network control for unmanned surface vessel course tracking , 2016, 2016 Sixth International Conference on Information Science and Technology (ICIST).

[8] Li Zhang,et al. Deep Convolutional Neural Network-Based Autonomous Marine Vehicle Maneuver , 2017, International Journal of Fuzzy Systems.