Robust adaptive path following of underactuated ships

By Khac Duc Do, Zhong-Ping Jiang, Jie Pan. Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2002

403 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/e72bf92ecff00da111fecbddfd33f5e593113130

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Zhouhua Peng
2. Jun Wang
3. Dan Wang
4. Kristin Ytterstad Pettersen
5. Xu Jin
6. Roberto Galeazzi
7. Pan-Mook Lee
8. Ji-Hong Li

In Ocean Engineering

1. Jun Wang
2. Zhouhua Peng
3. Dan Wang
4. Xu Jin
5. Kristin Ytterstad Pettersen
6. Roberto Galeazzi
7. Thor I. Fossen

Citations in the corpus

• Adaptive Fixed-Time Control for MIMO Nonlinear Systems With Asymmetric Output Constraints Using Universal Barrier Functions (2019, background): “For example, in two-dimensional trajectory tracking problems of ships or autonomous vehicles, the line-of-sight (LOS) distance tracking error should be small, but nonzero, in order to avoid singularity problems for the LOS angle tracking error [40], [41].”
• Distributed Maneuvering of Autonomous Surface Vehicles Based on Neurodynamic Optimization and Fuzzy Approximation (2018, background): “In [2], a robust path-following controller is developed for underactuated ships by using a nonlinear coordinate transformation.”
• Line-of-Sight Path Following for Dubins Paths With Adaptive Sideslip Compensation of Drift Forces (2015, methodology): “An alternative model-based approach for robust adaptive path following is proposed by Do et al. [9].”
• Technical communique: Point-to-point navigation of underactuated ships (2008, positive): “Moreover, the proposed scheme is much more concise and has a clear physical meaning of the tracking errors compared with those presented in previous works (Do et al., 2004; Do & Pan, 2005, 2006).”
• Output-Feedback Path-Following Control of Autonomous Underwater Vehicles Based on an Extended State Observer and Projection Neural Networks (2018, methodology): “Motion control scenario for marine vehicles include trajectorytracking [14]–[18], path-following [1], [2], [19]–[21], and formation control [3], [4], [8], [10], [22]–[25].”