Yanbin Teng

To address the challenge of balancing path-tracking performance and obstacle-avoidance safety for remotely operated vehicles (ROVs) in complex underwater environments, this paper proposes an autonomous control method integrating model predictive control (MPC), a disturbance-observer-based event-triggered mechanism, and dual-mode intelligent obstacle avoidance. First, a discrete-time prediction model is derived from the four-degree-of-freedom dynamic model of the ROV, and an MPC-based path-tracking controller is developed to ensure stable tracking of the reference trajectory. Second, a dual-mode obstacle-avoidance controller is designed for two typical hazardous scenarios: underwater obstacles and seabed peaks. Based on obstacle-risk assessment, forward guidance points, and a penalty function, the controller guides the ROV safely around obstacles and enables it to rapidly return to the reference trajectory after completing the avoidance maneuver. Furthermore, the disturbance-observer-based event-triggered mechanism improves computational efficiency while maintaining real-time control performance. Simulation shows that the minimum distances from obstacle safety boundaries and the seabed are 0.08 m and 3.54 m, respectively. The event-triggered ratios of the three axes are 6.57%, 15.46%, and 41.44% in simulation, and 4.90%, 20.62%, and 7.29% in pool experiments. Pool tests further verify safe obstacle avoidance with a minimum obstacle-boundary distance of 0.08 m and a minimum seabed distance of 0.33 m, demonstrating the feasibility of the proposed method.

Aiming at the problem of complex cluster constraints in the process of master slave AUV (Autonomous Underwater Vehicle) formation transformation and tracking in the wave environment, it is proposed to introduce the model predictive control (MPC) method to normalize a variety of complex constraints in the process of group formation navigation, unify the kinematic model and dynamic model, and introduce the normalized constraint equation. At the same time, the MPC formation transformation tracking control strategy is designed to achieve AUV formation transformation and tracking control under the unified model, which is verified through simulation tests to provide more effective technical support for AUV group formation.

Cargo transfer vessels (CTVs) are designed to transfer cargo from a floating production storage and offloading (FPSO) unit into conventional tankers. The dynamic positioning system allows the CTV to maintain a safe position relative to the FPSO unit using a flexible cargo transmission pipe, and the CTV tows the tanker during operating conditions. The operation mode can be considered a synchronization tracking control problem. In this paper, a synchronization control strategy is presented based on the virtual leader–follower configuration and an adaptive backstepping control method. The position and heading of the following vessel are proven to be able to globally exponentially converge to the virtual ship by the contraction theorem. Then, the optimization problem of the desired thrust command from the controller is solved through an improved firefly algorithm, which fully considers the physical characteristics of the azimuth thruster and the thrust forbidden zone caused by hydrodynamic interference. To validate the effectiveness of the presented synchronous following strategy and thrust allocation algorithm, a scale model experiment is carried out under a sea state of 4 in a seakeeping basin. The experimental results show that the CTV can effectively maintain a safe distance of 100 m with a maximum deviation of 3.78 m and an average deviation of only 0.99 m in the wave heading 180°, which effectively verifies that the control strategy proposed in this paper can achieve safe and cooperative operation between the CTV and the FPSO unit. To verify the advantages of the SAF algorithm in the thrust allocation, the SQP algorithm and PSO algorithm are used to compare the experimental results. The SAF algorithm outperforms the SQP and PSO algorithms in longitudinal and lateral forces, with the R-squared (R²) values of 0.9996 (yaw moment), 0.9878 (sway force), and 0.9596 (surge force) for the actual thrusts and control commands in the wave heading 180°. The experimental results can provide technical support to improve the safe operation of CTVs.

In order to realize more accurate and rapid recovery of Autonomous Underwater Vehicle (AUV), the L-shaped light array installed at the bottom of the fork-carrying-pole docking device is designed, and visual positioning algorithm based on the L-shaped light array is proposed. The L-shaped light array is identified by a camera installed on the abdomen of the AUV, the relative position deviation from the AUV and the light array is calculated, and the AUV is docked according to the obtained positional deviation. The monocular four-degree-of-freedom positioning algorithm for AUV solves the problem of missing part of the target light source. Judging the heading angle is improved when the AUV has a longitudinal inclination (heeling angle). The docking experiment of AUV recovery is carried out to verify the feasibility of L-shaped light array as visual guidance and the stability of positioning algorithm.

The main purpose of this paper is to study the heading controlling of an unmanned underwater vehicle (UUV) in low speed.