Adaptive Robust Finite-Time Trajectory Tracking Control of Fully Actuated Marine Surface Vehicles

By Ning Wang, Chunjiang Qian, Jing-Chao Sun, Yancheng Liu. IEEE Transactions on Control Systems Technology, 2016

317 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/e47cf6b9be5ee7980a02f00864574c17ec25716c

How researchers see this paper

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In Control and Systems Engineering

1. Wei He
2. Chenguang Yang
3. Changyin Sun
4. Zhao Yin
5. Shuzhi Sam Ge
6. Rongxin Cui
7. Haifeng Huang
8. Sanjay K. Sharma

Citations in the corpus

• Adaptive Neural Network Control of a Marine Vessel With Constraints Using the Asymmetric Barrier Lyapunov Function (2017, methodology): “NOWADAYS advanced control methods are broadly applied in modern offshore engineering applications, including the vessel system, mooring system, riser system, and installation system [1]–[4].”
• Adaptive Neural Network Control of AUVs With Control Input Nonlinearities Using Reinforcement Learning (2017, methodology): “In addition, an adaptive tracking control for fully actuated surface vessels employing a disturbance observer was designed in [33].”
• Adaptive Neural Network Control of a Robotic Manipulator With Time-Varying Output Constraints (2017, methodology): “The gain matrix K1 and K2 are chosen as K1 = diag[12, 12, 12], K2 = diag[8, 8, 8] which is the same as MB control, and σ1 = σ2 = σ3 = 0.”