Aiming at the trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) with unknown dynamic model parameters and time-varying ocean disturbances, a dynamic surface adaptive control strategy based on radial basis function neural network is proposed to complete the desired three-dimensional trajectory tracking. Firstly, an output redefinition method is used to solve the underactuated problem. Secondly, a dynamic surface control method is used to overcome the “explosion of complexity” problem in traditional backstepping control, and complexity of the control law design is simplified. At the same time, a nonlinear damping term is added in control law to overcome unknown external disturbances. Finally, unknown dynamic model parameters are approximated by a neural network, and the neural network weights are estimated online by an adaptive law. The Lyapunov theory is used to prove that all signals of closed-loop system are semi-globally uniformly bounded, and effectiveness of the control law is verified by comparative simulations.
Adaptive Neural Network Dynamic Surface Trajectory Tracking Control for Underactuated Autonomous Underwater Vehicles
2024-09-20
3326603 byte
Conference paper
Electronic Resource
English
Trajectory-tracking Control of Underactuated Underwater Vehicles
Springer Verlag | 2009
|British Library Online Contents | 2018
|