Abstract
A scheme to solve the course keeping problem of the unmanned surface vehicle with nonlinear and uncertain characteristics and unknown external disturbances is investigated in this article. The chattering existing in global fast terminal sliding mode controller in solving the course keeping problem of the unmanned surface vehicle with external disturbance is analyzed. To reduce the chattering and eliminate the influence of the unknown disturbance, an adaptive global fast terminal sliding mode controller based on radial basis function neural network is developed. The equivalent control that usually requires a precise model information of the system is computed using the radial basis function neural network. The weights of the neural network are online adjusted according to the adaptive law that is derived using Lyapunov method to ensure the stability of the closed-loop system. Using the online learning of the neural network, the nonlinear uncertainty of the system and the unknown disturbance of external environment are compensated, and the system chattering is reduced effectively as well. The simulation results demonstrate that the proposed controller can achieve a good performance regarding the fast response and smooth control.
Highlights
Unmanned surface vehicle (USV) plays a vital role in antisubmarine warfare, mine countermeasures, environmental detection, water sampling, personnel search and rescue in the ocean, and so on.[1,2,3] An effective course keeping controller is essential for the highly autonomous USV in navigation.[4]
The linear quadratic regulator theory,[7] model predictive control (MPC) technology,[8] backstepping method,[9,10,11] dynamic surface control technology,[12] fuzzy control,[13,14,15,16] neural network,[17,18,19] sliding mode control,[20,21,22,23] and so on, are all used to design the controller for course keeping of the ship
A backstepping controller combined with radial basis function (RBF) neural network has been proposed for course keeping of the ship with uncertainties and unknown external disturbances.[9]
Summary
Unmanned surface vehicle (USV) plays a vital role in antisubmarine warfare, mine countermeasures, environmental detection, water sampling, personnel search and rescue in the ocean, and so on.[1,2,3] An effective course keeping controller is essential for the highly autonomous USV in navigation.[4]. A backstepping controller combined with radial basis function (RBF) neural network has been proposed for course keeping of the ship with uncertainties and unknown external disturbances.[9] An adaptive nonlinear control strategy combined dynamic surface control and Nussbaum gain function with backstepping algorithm has been proposed for the course keeping of ships with parameter uncertainties and completely unknown control coefficient.[12] Fuzzy control is used to deal with the system with unknown dynamics.[27] A model reference adaptive robust fuzzy control algorithm has been presented for course keeping of the ship.[13] Considering the unknown yaw dynamics and measurement noises, a robust adaptive course keeping controller of USV has been developed with the aid of a predictor, neural networks, and a modified dynamic surface control technique.[17] An integrated nonlinear feedback course keeping controller is developed to improve the robustness of motion control in heavy sea states.[28] These aforementioned control strategies effectively improve the control performance of the nonlinear system and reduce the impact of system uncertainties and external disturbance on the system These algorithms are computationally complex and difficult to implement. The novel adaptive GFTSM controller combined with RBF neural network is proposed in the section “GFTSM-NN controller design for course keeping.” Simulation results and analyses of the proposed control system are shown in section “Simulation results.” In the last section, the whole article is concluded
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call