Depth and Heading Control of a Manta Robot Based on S-Plane Control

He, Yue; Xie, Yu; Pan, Guang; Cao, Yonghui; Huang, Qiaogao; Ma, Shumin; Zhang, Daili; Cao, Yong

doi:10.3390/jmse10111698

Cited by 8 publications

(7 citation statements)

References 42 publications

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“…The symbols such as X u̇ in formula (13) are the parameters of the added mass caused by the conversion, they are also called the additional mass. Due to the Coriolis-centrifugal force generated by the additional mass M A , the underwater platform will also consume energy while doing rotational movements.…”

Section: Kinematic Modeling Of Unmanned Underwater Platformsmentioning

confidence: 99%

“…Because the autonomous underwater vehicle (AUV) is a strongly nonlinear system, it is difficult to establish an accurate mathematical model for it, which brings various problems to the control of AUV [13]. Therefore, some researchers use fuzzy control and other control methods that do not require precise mathematical models to study the attitude control of underwater vehicles, Zhong et al [14] designed the AUV depth controller based on the fuzzy control theory, and realized the AUV's underwater deep-fixing cruise motion.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED: Research on motion and attitude control of unmanned underwater platform

Jin,

Miao,

Cui

2023

Preprint

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Unmanned underwater platforms are commonly used in ocean exploration and development to help researchers understand underwater conditions. A Model Reference Adaptive PID (MRAC-PID) control method is designed to solve the problem that a small unmanned underwater platform can’t carry different equipment to maintain motion and attitude stability in the harsh seabed environment. In this paper, the kinematic and dynamic system model of the platform in the stable state is set as the reference model of the control algorithm. When the model parameters of the controlled object change due to the environment or its factors, the controller will compare the control difference between the reference model and the controlled object. At the same time, the tracking control error will be fed back to the adaptive adjusting mechanism in real-time, so that the controller keeps iterating the control parameters. Through continuous optimization of control parameters, the control effect of the controlled object and the reference model can be kept consistent at all times so that the attitude of the unmanned underwater platform can be kept stable at all times. Compared with the traditional PID and fuzzy PID control algorithm, the algorithm developed in this paper improved the control performance indicators greatly. Even though the system model structure and parameters change with the operating environment, the MRAC-PID control algorithm is still effective and shows a very good robustness. The proposed control approach is also suitable for real-time application, which can provide a reference for the subsequent related researches.

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Section: Kinematic Modeling Of Unmanned Underwater Platformsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED: Research on motion and attitude control of unmanned underwater platform

Jin,

Miao,

Cui

2023

Preprint

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“…T c is the expected thrusting force (or torque) calculated by the control algorithm. T max is the maximum thrusting force (or torque) that the AUV can provide [38].…”

Section: S-plane Controllermentioning

confidence: 99%

“…Compared with the above methods, the classic S-plane method is more mature and effective in AUV control applications with higher control accuracy. By combining the PD structure with fuzzy logic, the classic S-plane method excels with a simple structure and few parameters requiring regulation, which has made it a commonly-used method for underwater vehicle control [38]. According to the control results from sea trials, the classic S-plane method provides an average overshoot of approximately 0.15 m/s in the velocity control and approximately 6 • in the heading control.…”

Section: Introductionmentioning

confidence: 99%

A LSSVR Interactive Network for AUV Motion Control

Jiang

Wan

Zhang

et al. 2023

JMSE

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In view of the requirements on control precision of autonomous underwater vehicles (AUVs) in different operations, the improvement of AUV motion control accuracy is the focus of this paper. In regard to the unsatisfying robustness of traditional control methods, an interactive network based on Least Square Support Vector Regression (LSSVR) is therefore put forward. The network completed the identification of the strong nonlinear AUV dynamic characteristics based on the LSSVR theory and by virtue of the interactions between the offline and online modules, it achieved offline design and online optimization of the AUV control law. In addition to contrastive numerical simulations and sea trials with the classic S-plane method in AUV velocity and heading control, the LSSVR network was also tested in path following and long-range cruise. The precision and robustness and of the proposed network were verified by the high-accuracy control results of the aforesaid simulations and trials. The network can be of practical use in AUV control especially under unfamiliar water conditions with access to a limited number of control samples or little information of the operation site.

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“…Among these control methods, the classic S-plane method has been proven to be an effective and practical method in extensive engineering practices. Combining the PD control structure with the fuzzy logic, the classic S-plane method has been verified in pool experiments and sea trials and can well meet the accuracy requirements in routine underwater operations [ 44 , 45 ]. Nevertheless, the impact of variable static loads is not taken into consideration by the classic S-plane method, which will degrade the control accuracy, especially in operations with high demands on control accuracy.…”

Section: Introductionmentioning

confidence: 99%

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

Jiang,

Zhang,

Wan

et al. 2023

Sensors

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A six degree-of-freedom (DOF) motion control system for docking with a deep submergence rescue vehicle (DSRV) test platform was the focus of this study. The existing control methods can meet the general requirements of underwater operations, but the complex structures or multiple parameters of some methods have prevented them from widespread use. The majority of the existing methods assume the heeling effect to be negligible and ignore it, achieving motion control in only four or five DOFs. In view of the demanding requirements regarding positions and inclinations in six DOFs during the docking process, the software and hardware architectures of the DSRV platform were constructed, and then sparse filtering technology was introduced for data smoothing. Based on the adaptive control strategy and with a consideration of residual static loads, an improved S-plane control method was developed. By converting the force (moment) calculated by the controller to the body coordinate system, the complexity of thrust allocation was effectively reduced, and the challenge of thrust allocation in the case of a high inclination during dynamic positioning was solved accordingly. The automatic control of the trimming angle and heeling angle was realized with the linkage system of the ballast tank and pump valve. A PID method based on an intelligent integral was proposed, which not only dealt with the integral “saturation” problem, but also reduced the steady-state error and overshooting. Water pool experiments and sea trials were carried out in the presence of water currents for six-DOF motion control. The responsiveness and precision of the control system were verified by the pool experiment and sea trial results and could meet the control requirements in engineering practice. The reliability and operational stability of the proposed control system were also verified in a long-distance cruise.

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Depth and Heading Control of a Manta Robot Based on S-Plane Control

Cited by 8 publications

References 42 publications

RETRACTED: Research on motion and attitude control of unmanned underwater platform

RETRACTED: Research on motion and attitude control of unmanned underwater platform

A LSSVR Interactive Network for AUV Motion Control

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

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