Robust Orientation-Sensitive Trajectory Tracking of Underactuated Autonomous Underwater Vehicles

He, Shiming; Kou, Liwei; Li, Yanjun; Xiang, Ji

doi:10.1109/tie.2020.3013790

Cited by 17 publications

(2 citation statements)

References 31 publications

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“…Yan et al provided a two-dimensional trajectory tracking method for a biomimetic underwater robot subjected to external disturbances, employing robust nonlinear model predictive control [ 25 ]. He et al proposed a robust non-smooth controller to achieve simultaneous tracking of the reference yaw angle and waypoints, which were validated through simulations for docking tasks and bridge pier detection tasks [ 26 ]. By constructing a dead-zone compensator and a two-layer cascaded tracking controller, Yu et al addressed the issue of strong oscillations in torque input and motion tracking of underwater vehicles caused by the inherent dead zone in the propeller thrusters [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

Toward the Intelligent, Safe Exploration of a Biomimetic Underwater Robot: Modeling, Planning, and Control

Wang,

et al. 2024

Biomimetics

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Safe, underwater exploration in the ocean is a challenging task due to the complex environment, which often contains areas with dense coral reefs, uneven terrain, or many obstacles. To address this issue, an intelligent underwater exploration framework of a biomimetic robot is proposed in this paper, including an obstacle avoidance model, motion planner, and yaw controller. Firstly, with the aid of the onboard distance sensors in robotic fish, the obstacle detection model is established. On this basis, two types of obstacles, i.e., rectangular and circular, are considered, followed by the obstacle collision model’s construction. Secondly, a deep reinforcement learning method is adopted to plan the plane motion, and the performances of different training setups are investigated. Thirdly, a backstepping method is applied to derive the yaw control law, in which a sigmoid function-based transition method is employed to smooth the planning output. Finally, a series of simulations are carried out to verify the effectiveness of the proposed method. The obtained results indicate that the biomimetic robot can not only achieve intelligent motion planning but also accomplish yaw control with obstacle avoidance, offering a valuable solution for underwater operation in the ocean.

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Section: Introductionmentioning

confidence: 99%

Toward the Intelligent, Safe Exploration of a Biomimetic Underwater Robot: Modeling, Planning, and Control

Wang,

et al. 2024

Biomimetics

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“…The research on unmanned underwater vehicles (UUVs) has been ongoing for many years [1][2][3][4][5][6][7]. There are wide applications for UUVs, such as ocean surveillance, fishing, and submarine construction surveys.…”

Section: Introductionmentioning

confidence: 99%

A hybrid tracking control strategy for an unmanned underwater vehicle aided with bioinspired neural dynamics

Yan

Yang

et al. 2022

IET Cyber-Syst and Robotics

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Tracking control has been a vital research topic in robotics. This paper presents a novel hybrid control strategy for an unmanned underwater vehicle (UUV) based on a bioinspired neural dynamics model. An enhanced backstepping kinematic control strategy is first developed to avoid sharp velocity jumps and provides smooth velocity commands relative to conventional methods. Then, a novel sliding mode control is proposed, which is capable of providing smooth and continuous torque commands free from chattering. In comparative studies, the proposed combined hybrid control strategy has ensured control signal smoothness, which is critical in real-world applications, especially for a UUV that needs to operate in complex underwater environments. K E Y W O R D S backstepping, bioinspired neural dynamics, sliding mode control, unmanned underwater vehicleThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Accurate trajectory tracking control for AUV under state constraints with a rapid stability dimensionality‐augmented state observer

Wang,

et al. 2024

Intl J Robust & Nonlinear

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A rapid stability dimensionality‐augmented state observer (RSDASO) based event‐driven control strategy is presented for the autonomous underwater vehicle (AUV) trajectory tracking issue, addressing state constraints, model uncertainty, limited communication resources and unknown external time‐varying disturbances. The first step is to develop a fast stability extended state observer to estimate the lumped disturbances and unmeasurable states of the system and ensure the estimation error converges in fixed time. Secondly, a fixed‐time AUV trajectory tracking control method is proposed to ensure that the tracking error of the system converges within a fixed time, based on the mentioned observer. Simultaneously, to reduce the communication resource usage by the system, an event‐triggered mechanism (ETM) is included in the control law. Lastly, simulation experiments verify the effectiveness of the process.

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Robust Orientation-Sensitive Trajectory Tracking of Underactuated Autonomous Underwater Vehicles

Cited by 17 publications

References 31 publications

Toward the Intelligent, Safe Exploration of a Biomimetic Underwater Robot: Modeling, Planning, and Control

Toward the Intelligent, Safe Exploration of a Biomimetic Underwater Robot: Modeling, Planning, and Control

A hybrid tracking control strategy for an unmanned underwater vehicle aided with bioinspired neural dynamics

Accurate trajectory tracking control for AUV under state constraints with a rapid stability dimensionality‐augmented state observer

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