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

YANG Shuo; WANG Honghui; LIU Xinyu; FANG Xin; LI Guanghao; LIU Guijie

doi:10.11993/j.issn.2096-3920.2024-0172

Volume 33 Issue 3

Jun 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(3): 420-432

YANG Shuo, WANG Honghui, LIU Xinyu, FANG Xin, LI Guanghao, LIU Guijie. Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 420-432. doi: 10.11993/j.issn.2096-3920.2024-0172

Citation:

YANG Shuo, WANG Honghui, LIU Xinyu, FANG Xin, LI Guanghao, LIU Guijie. Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 420-432. doi: 10.11993/j.issn.2096-3920.2024-0172

Citation:

YANG Shuo, WANG Honghui, LIU Xinyu, FANG Xin, LI Guanghao, LIU Guijie. Fixed Depth Control Strategy for Remotely Operated Vehicle Based on Improved Model Predictive Control Algorithm[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 420-432. doi: 10.11993/j.issn.2096-3920.2024-0172

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

doi: 10.11993/j.issn.2096-3920.2024-0172

College of Engineering, Ocean University of China, Qingdao 266100, China

Received Date: 2024-12-19
Accepted Date: 2025-02-08
Rev Recd Date: 2025-01-25

Available Online: 2025-05-27

Abstract

Abstract

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 the ROV by approximately 30% compared to traditional MPC and decreases the overshoot by about 10%. Under disturbance conditions, the maximum overshoot is reduced by about 27.7%. The proposed strategy significantly enhances the ROV’s fixed depth control performance, exhibiting higher tracking accuracy and better disturbance rejection capability.
- remotely operated vehicle,
- model predictive control,
- nonlinear marine predator algorithm,
- nonlinear disturbance observer,
- fixed depth control

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References(29)

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