Event-based adaptive horizon nonlinear model predictive control for trajectory tracking of marine surface vessel. (15th August 2022)

• Record Type: Journal Article
• Authors: Yuan, Shouzheng; Liu, Zhilin; Zheng, Linhe; Sun, Yuxin; Wang, Zhongxin
• Is Part Of: Ocean engineering. Volume 258(2022)
• Publication Date: 2022-08-15
• Subjects: Marine surface vessel -- Trajectory tracking -- Nonlinear model predictive control -- Adaptive horizon; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2022.111082
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: This study investigates the trajectory tracking problem of fully actuated marine surface vessels (MSVs). First, an optimal problem for trajectory tracking of MSV is established. Model uncertainty, time-varying environmental disturbances, and actuator saturation are considered in this problem. For the control algorithm, considering that the traditional nonlinear model predictive control (NMPC) algorithm requires large amounts of computational resources, it is not easy to transmit the control signal to the system in real-time in practice. Meanwhile, due to the limited bandwidth of the system, the large amount of data transmission may lead to congestion and loss of signal transmission. Therefore, a novel event-based adaptive horizon nonlinear model predictive control (EAHNMPC) scheme is proposed to relieve the computation burden and reduce the signal transmission frequency for the MSV's trajectory tracking. Moreover, stability proof of adaptive predictive horizon control is given, and simulation experiments under different cases are performed. The results show that the proposed scheme has better control performance and computational performance than other approaches. Highlights: An optimal problem for MSV's trajectory tracking considering model uncertainty, environmental disturbances, and actuator saturation is established. A novel event-based adaptive horizon nonlinear model predictive control scheme is proposed to balance control and computational performance. Stability proof for both increasing and decreasing predictive horizon cases is provided, and simulation experiments are performed.