Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm

• Published in: 水下无人系统学报 Vol. 33; no. 3; pp. 420 - 432
• Main Authors: Shuo YANG, Honghui WANG, Xinyu LIU, Xin FANG, Guanghao LI, Guijie LIU
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.06.2025
• Subjects: fixed depth control; model predictive control; nonlinear disturbance observer; nonlinear marine predator algorithm; remotely operated vehicle
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2024-0172

To address the issue of poor depth control stability in remotely operated vehicles(ROVs) with cables due to external disturbances in complex marine environments, a composite control strategy based on an improved model predictive control(MPC) was proposed. This strategy aims to achieve high-precision fixed depth control while significantly enhancing the robustness and disturbance rejection capability of ROVs under sudden external disturbances. First, a nonlinear marine predator algorithm(NMPA) was introduced to optimize key control parameters of MPC, ensuring fast and precise depth tracking of ROVs in complex marine environments. Secondly, by considering the impact of large external disturbances on the performance of the traditional MPC algorithm, the strategy incorporated a nonlinear disturbance observer(NDO) to compensate for external disturbances in real time, improving the ROV’s control performance and robustness. Simulation results demonstrate that the proposed strategy reduces the steady-state time of th