Robust composite neural dynamic surface control for the path following of unmanned marine surface vessels with unknown disturbances

• Main Authors: Jiangfeng Zeng, Lei Wan, Yueming Li, Ziyang Zhang, Yufei Xu, Gongrong Li
• Format: Article
• Language: English
• Published: SAGE Publishing 2018-07-01
• Series: International Journal of Advanced Robotic Systems
• Online Access: https://doi.org/10.1177/1729881418786646

This article presents a robust composite neural-based dynamic surface control design for the path following of unmanned marine surface vessels in the presence of nonlinearly parameterized uncertainties and unknown time-varying disturbances. Compared with the existing neural network-based dynamic surface control methods where only the tracking errors are commonly used for the neural network weight updating, the proposed scheme employs both the tracking errors and the prediction errors to construct the adaption law. Therefore, faster identification of the system dynamics and improved tracking accuracy are achieved. In particular, an outstanding advantage of the proposed neural network structure is simplicity. No matter how many neural network nodes are utilized, only one adaptive parameter that needs to be tuned online, which effectively reduces the computational burden and facilitates to implement the proposed controller in practice. The uniformly ultimate boundedness stability of the closed-loop system is established via Lyapunov analysis. Comparison studies are presented to demonstrate the effectiveness of the proposed composite neural-based dynamic surface control architecture.