Journal
INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS
Volume 15, Issue 6, Pages -Publisher
SAGE PUBLICATIONS INC
DOI: 10.1177/1729881418814699
Keywords
Unmanned surface vessels; path following; finite time; line-of-sight; minimal learning parameter; adaptive control
Categories
Funding
- National Natural Science Foundation of China [51879027, 51579024, 6137114, 51809028]
- Fundamental Research Funds for the Central Universities [3132016311, 3132018154]
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In the presence of unknown dynamics and input saturation, a finite-time predictor line-of-sight-based adaptive neural network scheme is presented for the path following of unmanned surface vessels. The proposed scheme merges with the guidance and the control subsystem of unmanned surface vessels together. A finite-time predictor-based line-of-sight guidance law is developed to ensure unmanned surface vessels effectively converging to and following the referenced path. Then, the path-following control laws are designed by combining neural network-based minimal learning parameter technique with backstepping method, where minimal learning parameter is applied to account for system nonparametric uncertainties. The key features of this scheme, first, the finite-time predictor errors are guaranteed; second, designed controllers are independent of the system model; and third, only required two parameters update online for each control law. The rigorous theory analysis verifies that all signals in the path-following guidance-control system are semi-globally uniformly ultimately bounded via Lyapunov stability theory. Simulation results illustrate the effectiveness and performance of the presented scheme.
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