Abstract
For an autonomous underwater vehicle (AUV), a nonlinear sliding-mode control based on linear-in-parameter neural network (NSMC-NN) is proposed to deal with the unknown dynamics and the external environmental disturbances and a first-order robust exact differentiator is introduced considering unknown velocities of an AUV. The sliding-mode surfaces of NSMC-NN can enter into the boundary layers after a period that depends on the design parameters. To demonstrate the feasibility of the proposed controller, simulation studies applying Omni-Directional Intelligent Navigator (ODIN) are carried out, compared with proportional–integral–derivative (PID) and the modified sliding-mode control (MSMC). The simulation results show that the presented control method can achieve the effective control performance.
Original language | English |
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Pages (from-to) | 677-692 |
Number of pages | 16 |
Journal | International Journal of Control |
Volume | 92 |
Issue number | 3 |
DOIs | |
State | Published - 4 Mar 2019 |
Keywords
- AUV
- neural network
- Nonlinear sliding-mode control
- robust exact differentiator
- unknown hydrodynamics