A LSSVR Interactive Network for AUV Motion Control

Jiang, Chunmeng; Wan, Lei; Zhang, Hongrui; Tang, J.; Wang, Jingguo; Li, Shupeng; Chen, Long; Wu, Gongxing; He, Baorong

doi:10.3390/jmse11061111

Cited by 5 publications

(4 citation statements)

References 55 publications

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“…As shown in Figure 2 , the hardware architecture is based on the PC104 embedded system with VxWorks as the real-time operating system [ 53 ]. The central processing unit (CPU) card, power card, A/D card, D/A card, digital input and output (DIO) card and serial port card [ 45 ] are integrated in the architecture. The motion sensors equipped with the DSRV include the Fiber-Optic Gyroscope (FOG), Doppler Velocity Log (DVL) and depth logger.…”

Section: Dsrv Platformmentioning

confidence: 99%

“…

is the expected thrusting force (or torque) calculated by the control method.

is the maximum thrusting force (or torque) that the AUV can provide [ 45 ].…”

Section: Control System Designmentioning

confidence: 99%

“…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%

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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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Section: Dsrv Platformmentioning

confidence: 99%

“…

is the expected thrusting force (or torque) calculated by the control method.

is the maximum thrusting force (or torque) that the AUV can provide [ 45 ].…”

Section: Control System Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

Jiang,

Zhang,

Wan

et al. 2023

Sensors

Self Cite

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show abstract

“…In variable universe fuzzy controllers, the introduction of a scaling universe part provides excellent robustness and adaptability, making the design of fuzzy controllers easier [24][25][26]. Compared with linear conventional PD control, the classical S-plane control algorithm with a nonlinear control surface is more suitable for the motion control of autonomous underwater robots [27]. Many scholars continue to deepen the demonstration and improvement of the S-plane control method and successfully apply it to various types of autonomous underwater robots [28,29].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Xu,

Zhang,

Zhong

2024

JMSE

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Autonomous Underwater Vehicles (AUVs) are widely used for the inspection of seabed pipelines. To address the issues of low trajectory tracking accuracy in AUV inspection processes due to uncertain ocean current disturbances, this paper designs a new dual-loop controller based on Model Predictive Control (MPC) and Variable Universe S-plane algorithms (S-VUD FLC, where VUD represents Variable Universe Discourse and FLC represents Fuzzy Logic Control) to achieve three-dimensional (3-D) trajectory tracking of an over-actuated AUV under uncertain ocean current disturbances. This paper uses MPC as the outer-loop position controller and S-VUD FLC as the inner-loop speed controller. The outer-loop controller generates desired speed instructions that are passed to the inner-loop speed controller, while the inner-loop speed controller generates control input and uses a direct logic thrust distribution method that approaches optimal energy consumption to distribute the thrust generated by the propellers to the over-actuated AUV, achieving closed-loop tracking of the entire trajectory. When designing the outer-loop MPC controller, the actual control input constraints of the system are considered, and control increments are introduced to reduce control model errors and the impact of uncertain external disturbances on the actual AUV model parameters. When designing the inner-loop S-VUD FLC, the strong robustness of the variable universe fuzzy controller and the easy construction characteristics of the S-plane algorithm are combined, and integral action is introduced to improve the system’s tracking accuracy. The stability of the outer loop controller is proven by the Lyapunov method, and the stability of the inner loop controller is verified by simulation. Finally, simulations show that the over-actuated AUV has fast tracking processes and high tracking result accuracy under uncertain ocean current disturbances, demonstrating the effectiveness of the designed dual-loop controller.

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Adaptive tracking control of underactuated AUV with historical navigation information and piecewise weighted fractional order integration

Liu,

Zhu,

Zhang

et al. 2024

Chinese Journal of Physics

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A LSSVR Interactive Network for AUV Motion Control

Cited by 5 publications

References 55 publications

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

Design and Verification of Deep Submergence Rescue Vehicle Motion Control System

Three-Dimensional Path Tracking of Over-Actuated AUVs Based on MPC and Variable Universe S-Plane Algorithms

Adaptive tracking control of underactuated AUV with historical navigation information and piecewise weighted fractional order integration

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