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- Title
Neural adaptive sliding mode controller for unmanned surface vehicle steering system.
- Authors
Lili Wan; Yixin Su; Huajun Zhang; Yongchuan Tang; Binghua Shi
- Abstract
Unmanned surface vehicle has the properties such as complexity, nonlinearity, time variability, and uncertainty, which lead to the difficulty of obtaining a precise kinematics model. A neural adaptive sliding mode controller for the unmanned surface vehicle steering system is developed based on the sliding mode control technique and the radial basis function neural network. In the new approach, two parallel radial basis function neural networks are used to reduce the influence of the system uncertainties and eliminate the dependency of the controller on the precise kinematics model of the system. Among these two radial basis function neural networks, one is used to approximate the unknown nonlinear yaw dynamics and the other is used to adjust the control gain as well as realize the variable gain sliding mode control. The weights of the two neural networks are trained online using the sliding surface variable and the control, where the Lyapunov method is used to derive the adaptive laws to ensure the stability of the whole closed-loop system. The proposed adaptive controller is suitable for the steering control at different cruising speeds with bounded external disturbances. The simulation results show that the proposed controller has a good control performance regarding the smooth control, fast response, and high accuracy.
- Subjects
SLIDING mode control; REMOTELY piloted vehicles; AUTOMOBILE steering; RADIAL basis functions; ADAPTIVE control systems
- Publication
Advances in Mechanical Engineering (Sage Publications Inc.), 2018, Vol 10, Issue 9, p1
- ISSN
1687-8132
- Publication type
Article
- DOI
10.1177/1687814018795523