RBF network based integral backstepping sliding mode control for USV

• Main Authors: Wanga Renqiang, Deng Hua, Miao Keyin, Zhao Yue, Du Jiabao
• Format: Article
• Language: English
• Published: EDP Sciences 2017-01-01
• Series: MATEC Web of Conferences
• Online Access: https://doi.org/10.1051/matecconf/201713900143

A kind of USV course RBF network control algorithm is putted forward, which is on the basis of integral backstepping sliding mode. First of all, an integrator sliding surface were designed with the sliding mode variable structure control technology. Secondly, radial basis function neural network was applied to approximate the system nonlinear function and uncertain parameters. Furthermore, a nonlinear damping law was introduced to overcome the bounded outside interference. Finally, on the basis of the above, the system control law was deduced by using the backstepping method. The simulation results show that the neural network can accurately approximate the nonlinear function and uncertain parameters, and the controller output is smooth and the output is not sensitive to perturbation of parameters. Therefore, the proposed algorithm is effective for USV course control.