Improved line-of-sight trajectory tracking control of under-actuated AUV subjects to ocean currents and input saturation

Xia, Yingkai; Xu, Kui; Li, Ye; Xu, Guohua; Xia, Xiang

doi:10.1016/j.oceaneng.2019.01.025

Cited by 97 publications

(38 citation statements)

References 45 publications

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“…be addressed (Elhaki and Shojaei, 2018;Shojaei, 2019;Wang et al, 2019) as well the potential issue of actuator saturation by utilizing anti-windup compensators (Galeani et al, 2009;Cui et al, 2016;Xia et al, 2019). In general, the preliminary trials have confirmed the suitability of a biologically plausible genetic algorithm to automatically tune specific sections or the entirety of the controller architecture of such an underwater vehicle.…”

mentioning

confidence: 78%

“…An additional challenge, which a truly bio-inspired robust system faces, is the ability to compensate for a changing environment, varying carried load, or potential partial system failure. The growing interest in the application of AUVs for exploratory, survey, and economic purposes has led to the development of numerous control strategies, attempting to improve the robustness of such systems to time-variable ocean currents or waves (Elhaki and Shojaei, 2018;Zhong et al, 2018;Shojaei, 2019;Wang et al, 2019;Xia et al, 2019). Recently, the use of neural networks has become increasingly widespread in such control applications to account for changing weight distributions of moving manipulators and otherwise unmodeled hydrodynamics (Hernández-Alvarado et al, 2016;Elhaki and Shojaei, 2018;Shojaei, 2019).…”

Section: Biological Inspirationmentioning

confidence: 99%

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SAUV—A Bio-Inspired Soft-Robotic Autonomous Underwater Vehicle

2020

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Autonomous and remotely operated underwater vehicles allow us to reach places which have previously been inaccessible and perform complex repair, exploration and analysis tasks. As their navigation is not infallible, they may cause severe damage to themselves and their often fragile surroundings, such as flooded caves, coral reefs, or even accompanying divers in case of a collision. In this study, we used a shallow neural network, consisting of interlinking PID controllers, and trained by a genetic algorithm, to control a biologically inspired AUV with a soft and compliant exoskeleton. Such a compliant structure is a versatile and passive solution which reduces the accelerations induced by collisions to 56% of the original mean value acting upon the system, thus, notably reducing the stress on its components and resulting reaction forces on its surroundings. The segmented structure of this spherical exoskeleton protects the encased system without limiting the use of cameras, sensors or manipulators.

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mentioning

confidence: 78%

Section: Biological Inspirationmentioning

confidence: 99%

SAUV—A Bio-Inspired Soft-Robotic Autonomous Underwater Vehicle

2020

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“…where σ 10 is designed positive constant, and the design parameters are chosen to satisfy the condition k 1 − 1 2σ 10 > 0. It can be concluded from (19) thatV 1 < 0 as long as z 2 1 > σ 10 2 z 2 2 (k 1 − 1 2σ 10 ). Consequently, the decrease of…”

Section: B Three-layer Recurrent Rbfnnmentioning

confidence: 99%

“…During the past few decades, ocean exploration has experienced a booming development thanks to the advances in marine equipment technology [1], [2]. Among the wide variety of marine equipment, marine platform has become one of the most commonly used structures for offshore exploration and production owing to the excellent structural stability and carrying capability [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Trajectory Tracking Control of a Cable-Driven Underwater Vehicle on a Tension Leg Platform

Xia

et al. 2019

IEEE Access

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This paper focuses on the dynamic modeling, controller design, and simulation verification of a new cable-driven underwater vehicle model system, which is proposed for future application on a tension leg platform. Compared with conventional underwater vehicles, the proposed cable-driven underwater vehicle model system has a unique structure and subjects to not only unknown underwater disturbances but also time-varying nonlinear cable tractions. To achieve accurate and robust trajectory tracking control, a universal high-order nonlinear dynamic model is established with multisource uncertainties, and an integrated estimation-based adaptive backstepping terminal sliding mode controller is designed. The proposed controller utilizes the integrated estimation method to handle the multisource unknown uncertainties, where recurrent radial basis function neural network and disturbance observer are employed for the estimations of uncertainties, and adaptive robust methods are utilized for compensations of estimation errors. The backstepping terminal sliding mode control method is utilized to improve the tracking performance and converging rate. In addition, the issue of ''explosion of complexity'' occurred in a traditional backstepping design is tackled by an adaptive control method, and the input saturation problem is solved by antiwindup compensator. Based on the Lyapunov analysis, all closed-loop signals are proved to be uniformly ultimately bounded. The numerical simulations show that the developed controller is not only robust against environmental disturbances and adaptive to unknown time-varying uncertainties but also able to steer the trajectory tracking errors along the prescribed transient, thus leading to effective cable-driven underwater vehicle model system control. INDEX TERMS Cable-driven underwater vehicle model system, trajectory tracking control, integrated estimation, adaptive backstepping terminal sliding mode control, input saturation.

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“…Energy efficiency is an important characteristic of path planning algorithms for autonomous systems. In particular, deployment of Unmanned Surface Vehicle (USV) (Lv et al, 2019) and Unmanned Underwater Vehicle (UUV) (Palomeras et al, 2018;Xia et al, 2019) for missions with various environmental disturbances and uncertainties in presence make path planning more challenging, because of the existence of uncertain, inaccurate and dynamic environmental information. The path planning algorithms of USVs and UUVs aim to optimise the following aspects: travel time, energy consumption and safety.…”

Section: Introductionmentioning

confidence: 99%

Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Niu

Chai

et al. 2020

Ocean Engineering

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Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstras searching, coastline expanding and genetic algorithm. The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also im

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Improved line-of-sight trajectory tracking control of under-actuated AUV subjects to ocean currents and input saturation

Cited by 97 publications

References 45 publications

SAUV—A Bio-Inspired Soft-Robotic Autonomous Underwater Vehicle

SAUV—A Bio-Inspired Soft-Robotic Autonomous Underwater Vehicle

Adaptive Trajectory Tracking Control of a Cable-Driven Underwater Vehicle on a Tension Leg Platform

Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

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