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16 pages, 3193 KiB

Open AccessArticle

Neural Network-Based Adaptive Sigmoid Circular Path-Following Control for Underactuated Unmanned Surface Vessels under Ocean Disturbances

by Yi Ren, Lei Zhang, Wenbin Huang and Xi Chen

J. Mar. Sci. Eng. 2023, 11(11), 2160; https://doi.org/10.3390/jmse11112160 - 13 Nov 2023

Cited by 2 | Viewed by 805

Abstract

This study describes a circular curve path-following controller for an underactuated unmanned surface vessel (USV) experiencing unmodeled dynamics and external disturbances. Initially, a three degrees of freedom kinematic model of the USV is proposed for marine environmental disturbances and internal model parameter deterrence. [...] Read more.

This study describes a circular curve path-following controller for an underactuated unmanned surface vessel (USV) experiencing unmodeled dynamics and external disturbances. Initially, a three degrees of freedom kinematic model of the USV is proposed for marine environmental disturbances and internal model parameter deterrence. Then, the circular path guidance law and controller are designed to ensure that the USV can move along the desired path. During the design process, a proportional derivative (PD)-based sigmoid fuzzy function is applied to adjust the guidance law. To accommodate unknown system dynamics and perturbations, a radial basis function neural network and adaptive updating laws are adopted to design the surge motion and yaw motion controllers, estimating the unmodeled hydrodynamic coefficients and external disturbances. Theoretical analysis shows that tracking errors are uniformly ultimately bounded (UUB), and the closed-loop system is asymptotically stable. Finally, the simulation results show that the proposed controller can achieve good control effects while ensuring tracking accuracy and demonstrating satisfactory disturbance rejection capability. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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20 pages, 14975 KiB

Open AccessArticle

Collaborative Framework for Underwater Object Detection via Joint Image Enhancement and Super-Resolution

by Xun Ji, Guo-Peng Liu and Cheng-Tao Cai

J. Mar. Sci. Eng. 2023, 11(9), 1733; https://doi.org/10.3390/jmse11091733 - 01 Sep 2023

Viewed by 905

Abstract

Underwater object detection (UOD) has attracted widespread attention, being of great significance for marine resource management, underwater security and defense, underwater infrastructure inspection, etc. However, high-quality UOD tasks often encounter challenges such as image quality degradation, complex backgrounds, and occlusions between objects at [...] Read more.

Underwater object detection (UOD) has attracted widespread attention, being of great significance for marine resource management, underwater security and defense, underwater infrastructure inspection, etc. However, high-quality UOD tasks often encounter challenges such as image quality degradation, complex backgrounds, and occlusions between objects at different scales. This paper presents a collaborative framework for UOD via joint image enhancement and super-resolution to address the above problems. Specifically, a joint-oriented framework is constructed incorporating underwater image enhancement and super-resolution techniques. The proposed framework is capable of generating a detection-favoring appearance to provide more visual cues for UOD tasks. Furthermore, a plug-and-play self-attention mechanism, termed multihead blurpooling fusion network (MBFNet), is developed to capture sufficient contextual information by focusing on the dependencies between multiscale feature maps, so that the UOD performance of our proposed framework can be further facilitated. A comparative study on the popular URPC2020 and Brackish datasets demonstrates the superior performance of our proposed collaborative framework, and the ablation study also validates the effectiveness of each component within the framework. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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18 pages, 8161 KiB

Open AccessArticle

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

by Yi Ren, Lei Zhang, Yanqing Ying, Shuyuan Li and Yueqi Tang

J. Mar. Sci. Eng. 2023, 11(9), 1717; https://doi.org/10.3390/jmse11091717 - 31 Aug 2023

Cited by 3 | Viewed by 776

Abstract

This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack [...] Read more.

This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack of control in the sway channel. Secondly, nonlinear behavior stemming from saturation constraints is not always differentiable. Regarding this, a smooth dead-zone-based model was conducted to fit the behavior, leaving a relatively simple actuator model. Then, an improved prescribed time–prescribed performance function (PTPPF) and error transformation method were utilized to propose a model-parameter-free control algorithm that guarantees user-defined constrained boundaries while ensuring all tracking errors converge within small domains before a preassigned settling time. The theoretical analysis was conducted by the initial value theorem, Lyapunov’s second method, and proof by contradiction, followed by comparative simulation results that verified the effectiveness of the proposed control scheme. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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19 pages, 5632 KiB

Open AccessArticle

AUIE–GAN: Adaptive Underwater Image Enhancement Based on Generative Adversarial Networks

by Fengxu Guan, Siqi Lu, Haitao Lai and Xue Du

J. Mar. Sci. Eng. 2023, 11(7), 1476; https://doi.org/10.3390/jmse11071476 - 24 Jul 2023

Cited by 1 | Viewed by 1110

Abstract

Underwater optical imaging devices are often affected by the complex underwater environment and the characteristics of the water column, which leads to serious degradation and distortion of the images they capture. Deep learning-based underwater image enhancement (UIE) methods reduce the reliance on physical [...] Read more.

Underwater optical imaging devices are often affected by the complex underwater environment and the characteristics of the water column, which leads to serious degradation and distortion of the images they capture. Deep learning-based underwater image enhancement (UIE) methods reduce the reliance on physical parameters in traditional methods and have powerful fitting capabilities, becoming a new baseline method for UIE tasks. However, the results of these methods often suffer from color distortion and lack of realism because they tend to have poor generalization and self-adaptation capabilities. Generating adversarial networks (GANs) provides a better fit and shows powerful capabilities on UIE tasks. Therefore, we designed a new network structure for the UIE task based on GANs. In this work, we changed the learning of the self-attention mechanism by introducing a trainable weight to balance the effect of the mechanism, improving the self-adaptive capability of the model. In addition, we designed a feature extractor based on residuals with multi-level residuals for better feature recovery. To further improve the performance of the generator, we proposed a dual path discriminator and a loss function with multiple weighted fusions to help model fitting in the frequency domain, improving image quality. We evaluated our method on the UIE task using challenging real underwater image datasets and a synthetic image dataset and compared it to state-of-the-art models. The method ensures increased enhancement quality, and the enhancement effect of the model for different styles of images is also relatively stable. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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25 pages, 12997 KiB

Open AccessArticle

Cross-Coupled Dynamics and MPA-Optimized Robust MIMO Control for a Compact Unmanned Underwater Vehicle

by Ahsan Tanveer and Sarvat Mushtaq Ahmad

J. Mar. Sci. Eng. 2023, 11(7), 1411; https://doi.org/10.3390/jmse11071411 - 14 Jul 2023

Cited by 4 | Viewed by 1071

Abstract

A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing [...] Read more.

A compact, 3-degrees-of-freedom (DoF), low-cost, remotely operated unmanned underwater vehicle (UUV), or MicroROV, is custom-designed, developed, instrumented, and interfaced with a PC for real-time data acquisition and control. The nonlinear equations of motion (EoM) are developed for the under-actuated, open-frame, cross-coupled MicroROV utilizing the Newton-Euler approach. The cross-coupling between heave and yaw motion, an important dynamic of a class of compact ROVs that is barely reported, is investigated here. This work is thus motivated towards developing an understanding of the physics of the highly coupled compact ROV and towards developing model-based stabilizing controllers. The linearized EoM aids in developing high-fidelity experimental data-driven transfer function models. The coupled heave-yaw transfer function model is improved to an auto-regressive moving average with exogenous input (ARMAX) model structure. The acquired models facilitate the use of the multi-parameter root-locus (MPRL) technique to design baseline controllers for a cross-coupled multi-input, multi-output (MIMO) MicroROV. The controller gains are further optimized by employing an innovative Marine Predator Algorithm (MPA). The robustness of the designed controllers is gauged using gain and phase margins. In addition, the real-time controllers were deployed on an onboard embedded system utilizing Simulink′s automatic C++ code generation capabilities. Finally, pool tests of the MicroROV demonstrate the efficacy of the proposed control strategy. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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21 pages, 6820 KiB

Open AccessArticle

Intelligent Fault Diagnosis of Variable-Condition Motors Using a Dual-Mode Fusion Attention Residual

by Fengyun Xie, Gang Li, Wang Hu, Qiuyang Fan and Shengtong Zhou

J. Mar. Sci. Eng. 2023, 11(7), 1385; https://doi.org/10.3390/jmse11071385 - 07 Jul 2023

Cited by 2 | Viewed by 843

Abstract

Electric motors play a crucial role in ship systems. Detecting potential issues with electric motors is a critical aspect of ship fault diagnosis. Fault diagnosis in motors is often challenging due to limited and noisy vibration signals. Existing deep learning methods struggle to [...] Read more.

Electric motors play a crucial role in ship systems. Detecting potential issues with electric motors is a critical aspect of ship fault diagnosis. Fault diagnosis in motors is often challenging due to limited and noisy vibration signals. Existing deep learning methods struggle to extract the underlying correlation between samples while being susceptible to noise interference during the feature extraction process. To overcome these issues, this study proposes an intelligent bimodal fusion attention residual model. Firstly, the vibration signal to be encoded undergoes demodulation and is divided into high and low frequencies using the IEEMD (Improved Ensemble Empirical Mode Decomposition) composed of the EEMD (Ensemble Empirical Mode Decomposition) and the MASM (the Mean of the Standardized Accumulated Modes). Subsequently, the high-frequency component is effectively denoised using the wavelet packet threshold method. Secondly, current data and vibration signals are transformed into two-dimensional images using the Gramian Angular Summation Field (GASF) and aggregated into a bimodal Gramian Angle Field diagram. Finally, the proposed model incorporates the Self-Attention Squeeze-and-Excitation Networks (SE) mechanism with the Swish activation function and utilizes the ResNeXt architecture with a Dropout layer to identify and diagnose faults in the multi-mode fusion dataset of motors under various working conditions. Based on the experimental results, a comprehensive discussion and analysis were conducted to evaluate the performance of the proposed intelligent bimodal fusion attention residual model. The results demonstrated that, in comparison to traditional methods and other deep learning models, the proposed model effectively utilized multimodal data, thereby enhancing the accuracy and robustness of fault diagnosis. The introduction of attention mechanisms and residual learning enable the model to focus more effectively on crucial modal data and learn the correlations between modalities, thus improving the overall performance of fault diagnosis. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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18 pages, 5719 KiB

Open AccessArticle

Development of an Automated Spare-Part Management Device for Ship Controlled by Raspberry-Pi Microcomputer Based on Image-Progressing & Transfer-Learning

by Chang-Min Lee, Hee-Joo Jang and Byung-Gun Jung

J. Mar. Sci. Eng. 2023, 11(5), 1015; https://doi.org/10.3390/jmse11051015 - 10 May 2023

Viewed by 1266

Abstract

As the development of autonomous ships is underway in the maritime industry, the automation of ship spare part management has become an important issue. However, there has been little development of dedicated devices or applications for ships. This study aims to develop a [...] Read more.

As the development of autonomous ships is underway in the maritime industry, the automation of ship spare part management has become an important issue. However, there has been little development of dedicated devices or applications for ships. This study aims to develop a Raspberry Pi-based embedded application that identifies the type and quantity of spare parts using a transfer learning model and image processing algorithm suitable for ship spare part recognition. A newly improved image processing algorithm was used to select a transfer learning model that balances accuracy and training speed through training and validation on a real spare parts dataset, achieving a prediction accuracy of 98.2% and a training time of 158 s. The experimental device utilizing this model used a camera to identify the type and quantity of spare parts on an actual ship. It displayed the spare parts list on a remotely connected computer. The ASSM (Automated Ship Spare-Part Management) device utilizing image processing and transfer learning is a new technology that successfully automates spare part management. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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21 pages, 1633 KiB

Open AccessArticle

Integral Sliding Mode-Based Fault-Tolerant Control for Dynamic Positioning of Unmanned Marine Vehicles Based on a T-S Fuzzy Model

by Yang Wang, Li-Ying Hao, Tieshan Li and C. L. Philip Chen

J. Mar. Sci. Eng. 2023, 11(2), 370; https://doi.org/10.3390/jmse11020370 - 07 Feb 2023

Cited by 4 | Viewed by 1323

Abstract

This paper investigates a fault-tolerant control problem for the dynamic positioning of unmanned marine vehicles based on a Takagi–Sugeno (T-S) fuzzy model using an integral sliding mode scheme. First, the T-S fuzzy model of an unmanned marine vehicle is established by taking the [...] Read more.

This paper investigates a fault-tolerant control problem for the dynamic positioning of unmanned marine vehicles based on a Takagi–Sugeno (T-S) fuzzy model using an integral sliding mode scheme. First, the T-S fuzzy model of an unmanned marine vehicle is established by taking the yaw angle variable range into account. An integral sliding mode control scheme combined with the

H_{\infty}

performance index is then developed to attenuate the initial influence of thruster faults and ocean disturbances. The unknown nonlinear function is approximated using a fuzzy logic system based on a representation of marine data, which provides a good tradeoff between resolution of the unknown nonlinear term approximation and computational complexity for marine engineering by adjusting the number of fuzzy logic system rules. In addition, the fault estimation information is utilized to design the sliding mode surface on the basis of an adaptive mechanism and a matrix full rank decomposition technique, which reduces conservatism. The validity of the proposed approach is finally demonstrated by an analysis of simulation results using a typical floating production vessel model. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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20 pages, 14523 KiB

Open AccessArticle

Object Extraction Algorithm for the First-Frame Image of Unmanned Surface Vehicles Based on a Radar-Photoelectric System

by Qingze Yu, Yumin Su and Renran Zhang

J. Mar. Sci. Eng. 2023, 11(2), 344; https://doi.org/10.3390/jmse11020344 - 04 Feb 2023

Cited by 1 | Viewed by 1079

Abstract

The radar-photoelectric system is a perception system to detect the surrounding environment based on marine radar and a photoelectric device. Mast obscuration, green water, and multi-object scenes are special scenes that appear in the first-frame image during the navigation of unmanned surface vehicles. [...] Read more.

The radar-photoelectric system is a perception system to detect the surrounding environment based on marine radar and a photoelectric device. Mast obscuration, green water, and multi-object scenes are special scenes that appear in the first-frame image during the navigation of unmanned surface vehicles. The perception system cannot accurately obtain the object information in mast obscuration and green water scenes. The radar-guided object cannot be stably extracted from the first-frame image in multi-object scenes. Therefore, this paper proposes an object extraction algorithm for the first-frame image of unmanned surface vehicles based on a radar-photoelectric system. The algorithm realizes the field-of-view adaptation to solve the problem that the features of the radar-guided object are incomplete in the first-frame image and improve the detection accuracy of the local features by 16.8%. The algorithm realizes the scene recognition of the first-frame image to improve the robustness of object tracking. In addition, the algorithm achieves the stable extraction of the radar-guided object in multi-object scenes. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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17 pages, 2939 KiB

Open AccessArticle

Nonlinear Extended State Observer-Based Distributed Formation Control of Multiple Vessels with Finite-Time Prescribed Performance

by Shasha Wang, Dongchen Dai, Dan Wang and Yulong Tuo

J. Mar. Sci. Eng. 2023, 11(2), 321; https://doi.org/10.3390/jmse11020321 - 02 Feb 2023

Cited by 1 | Viewed by 1206

Abstract

In the presence of unmeasurable velocities and system uncertainties, the distributed formation control problem is investigated in this paper for multiple vessels. A robust formation controller is proposed by incorporating an extended state observer (ESO) and finite-time prescribed performance function (FTPPF). Firstly, a [...] Read more.

In the presence of unmeasurable velocities and system uncertainties, the distributed formation control problem is investigated in this paper for multiple vessels. A robust formation controller is proposed by incorporating an extended state observer (ESO) and finite-time prescribed performance function (FTPPF). Firstly, a nonlinear ESO is designed to estimate the unmeasurable velocities and system uncertainties. Subsequently, a novel FTPPF is designed to improve the dynamic performance of multi-vessel formation systems, where the upper bound of the convergence time and the constraint bounds can be set in advance based on the actual circumstances. Then, the proposed ESO and FTPPF are applied to the distributed formation controller design for multiple vessels. The proposed formation control scheme can maintain the multiple vessels in an expected formation with high tracking accuracy, a faster convergence speed, and smaller fluctuations. Finally, the performance of the proposed control method is verified by theory analysis and simulations. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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19 pages, 4370 KiB

Open AccessArticle

Quantized Sliding Mode Fault-Tolerant Control for Unmanned Marine Vehicles with Thruster Saturation

by Li-Ying Hao and Zhi-Hao Zhao

J. Mar. Sci. Eng. 2023, 11(2), 309; https://doi.org/10.3390/jmse11020309 - 01 Feb 2023

Viewed by 1190

Abstract

In this paper, the sliding mode technique is used to study the quantized fault-tolerant control of unmanned marine vehicles with thruster saturation. Firstly, the sliding mode surface is constructed according to the full rank decomposition of input matrix, and the stability of sliding [...] Read more.

In this paper, the sliding mode technique is used to study the quantized fault-tolerant control of unmanned marine vehicles with thruster saturation. Firstly, the sliding mode surface is constructed according to the full rank decomposition of input matrix, and the stability of sliding mode is guaranteed by linear matrix inequalities. An improved dynamic adjustment scheme of quantization parameter is proposed. Compared with the original adjustment scheme, the relationship between quantization parameter and desired targets is increased, so that the adjustment range of quantization parameters is more comprehensive. The sliding mode controller is combined with quantization parameter adjustment strategy to ensure the asymptotic stability of unmanned marine vehicles system. In addition, compared with the existing research results of quantitative fault tolerance problem without considering saturation, this paper gives a result of the domain of attraction affected by the fault of the thruster. Finally, the superiority of the proposed method is verified by simulation comparison. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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19 pages, 1821 KiB

Open AccessArticle

Markovian-Jump Reinforcement Learning for Autonomous Underwater Vehicles under Disturbances with Abrupt Changes

by Wenjie Lu, Yongquan Huang and Manman Hu

J. Mar. Sci. Eng. 2023, 11(2), 285; https://doi.org/10.3390/jmse11020285 - 27 Jan 2023

Viewed by 1164

Abstract

This paper studies the position regulation problems of an Autonomous Underwater Vehicle (AUV) subject to external disturbances that may have abrupt variations due to some events, e.g., water flow hitting nearby underwater structures. The disturbing forces may frequently exceed the actuator capacities, necessitating [...] Read more.

This paper studies the position regulation problems of an Autonomous Underwater Vehicle (AUV) subject to external disturbances that may have abrupt variations due to some events, e.g., water flow hitting nearby underwater structures. The disturbing forces may frequently exceed the actuator capacities, necessitating a constrained optimization of control inputs over a future time horizon. However, the AUV dynamics and the parameters of the disturbance models are unknown. Estimating the Markovian processes of the disturbances is challenging since it is entangled with uncertainties from AUV dynamics. As opposed to a single-Markovian description, this paper formulates the disturbed AUV as an unknown Markovian-Jump Linear System (MJLS) by augmenting the AUV state with the unknown disturbance state. Based on an observer network and an embedded solver, this paper proposes a reinforcement learning approach, Disturbance-Attenuation-net (MDA–net), for attenuating Markovian-jump disturbances and stabilizing the disturbed AUV. MDA–net is trained based on the sensitivity analysis of the optimality conditions and is able to estimate the disturbance and its transition dynamics based on observations of AUV states and control inputs online. Extensive numerical simulations of position regulation problems and preliminary experiments in a tank testbed have shown that the proposed MDA–net outperforms the existing DOB–net and a classical approach, Robust Integral of Sign of Error (RISE). Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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22 pages, 4754 KiB

Open AccessArticle

Anti-Disturbance Lyapunov-Based Model Predictive Control for Trajectory Tracking of Dynamically Positioned Ships

by Quan Zhang and Chen Guo

J. Mar. Sci. Eng. 2023, 11(2), 281; https://doi.org/10.3390/jmse11020281 - 26 Jan 2023

Cited by 1 | Viewed by 1421

Abstract

Trajectory tracking is a fundamental task of the dynamic positioning (DP) system. This paper studies the problem of trajectory tracking of DP ships constrained by control inputs under environmental disturbances. To solve this problem, we develop a novel anti-disturbance Lyapunov-based model predictive control [...] Read more.

Trajectory tracking is a fundamental task of the dynamic positioning (DP) system. This paper studies the problem of trajectory tracking of DP ships constrained by control inputs under environmental disturbances. To solve this problem, we develop a novel anti-disturbance Lyapunov-based model predictive control (ADLMPC) scheme. Firstly, an extended state observer (ESO) is designed to estimate environmental disturbances. By combining the ESO with Lyapunov-based model predictive control, the ADLMPC scheme is devised. Secondly, a virtual controller which satisfies input constraints is developed by backstepping and the auxiliary dynamic system, and it is integrated into the Lyapunov contraction constraint in ADLMPC. We show that if the parameters for the virtual controller are appropriately determined, the recursive feasibility of ADLMPC is theoretically guaranteed, and the uniform ultimate boundedness of all signals in the trajectory tracking control system is achieved. Finally, the simulation results display the efficacy and superiorities of the ADLMPC scheme. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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17 pages, 1321 KiB

Open AccessArticle

Nonlinear Model Predictive Control of Shipboard Boom Cranes Based on Moving Horizon State Estimation

by Yuchi Cao, Tieshan Li and Liying Hao

J. Mar. Sci. Eng. 2023, 11(1), 4; https://doi.org/10.3390/jmse11010004 - 20 Dec 2022

Cited by 6 | Viewed by 1376

Abstract

As important equipment in offshore engineering and freight transportation, shipboard cranes, working in non-inertial coordination systems, are complicated nonlinear systems with strong couplings and typical underactuation. To tackle the challenges in the controller design for shipboard boom cranes, which is a representative type [...] Read more.

As important equipment in offshore engineering and freight transportation, shipboard cranes, working in non-inertial coordination systems, are complicated nonlinear systems with strong couplings and typical underactuation. To tackle the challenges in the controller design for shipboard boom cranes, which is a representative type of shipboard cranes, a comprehensive framework embedding moving horizon estimation (MHE) in model predictive control (MPC) is constructed in this paper while considering disturbances and noise. By utilizing MHE, velocity information can be estimated with high precision even though this is influenced by disturbances and measurement noises. This expected superiority can greatly ease the difficulties in directly measuring all states of shipboard boom cranes. Then, the estimated information can be passed to MPC to derive the optimal control law by solving a constrained optimal problem. During this process, the physical limits of shipboard boom cranes are fully considered. Therefore, the practicability of the proposed framework is highly suitable for the actual requirements of shipboard boom cranes. Finally, the framework is verified by designing three typical scenarios with different disturbances and/or noises. Comparisons with other control approaches are also performed to demonstrate the effectiveness. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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15 pages, 1083 KiB

Open AccessArticle

Learning-Based Nonlinear Model Predictive Controller for Hydraulic Cylinder Control of Ship Steering System

by Xiaolong Tang, Changjie Wu and Xiaoyan Xu

J. Mar. Sci. Eng. 2022, 10(12), 2033; https://doi.org/10.3390/jmse10122033 - 19 Dec 2022

Cited by 5 | Viewed by 2031

Abstract

The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with [...] Read more.

The steering mechanism of ship steering gear is generally driven by a hydraulic system. The precise control of the hydraulic cylinder in the steering mechanism can be achieved by the target rudder angle. However, hydraulic systems are often described as nonlinear systems with uncertainties. Since the system parameters are uncertain and system performances are influenced by disturbances and noises, the robustness cannot be satisfied by approximating the nonlinear theory by a linear theory. In this paper, a learning-based model predictive controller (LB-MPC) is designed for the position control of an electro-hydraulic cylinder system. In order to reduce the influence of uncertainty of the hydraulic system caused by the model mismatch, the Gaussian process (GP) is adopted, and also the real-time input and output data are used to improve the model. A comparative simulation of GP-MPC and MPC is performed assuming that the interference and uncertainty terms are bounded. Consequently, the proposed control strategy can effectively improve the piston position quickly and precisely with multiple constraint conditions. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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16 pages, 5715 KiB

Open AccessArticle

Fault-Tolerant Thrust Allocation with Thruster Dynamics for a Twin-Waterjet Propelled Vessel

by Zijing Xu, Roberto Galeazzi and Jingqi Yuan

J. Mar. Sci. Eng. 2022, 10(12), 1983; https://doi.org/10.3390/jmse10121983 - 13 Dec 2022

Viewed by 1254

Abstract

The availability of the propulsion system is of primary importance to ensure safe and stable operations of marine crafts, both during transit and station keeping. Diminished propulsion efficiency could impair the ability of a vessel to maintain speed and course and possibly lead [...] Read more.

The availability of the propulsion system is of primary importance to ensure safe and stable operations of marine crafts, both during transit and station keeping. Diminished propulsion efficiency could impair the ability of a vessel to maintain speed and course and possibly lead to a drifting craft. The waterjet’s propulsion efficiency is affected by several factors such as cavitation, erosion, vibration and noise emission. This paper addresses the design of a fault-tolerant thrust allocation algorithm able to maintain the seaworthiness of a twin-waterjet marine craft in the presence of a severe power loss in one of the waterjets. The proposed solution combines a load torque estimator with an optimization routine that accounts for the power limits when a waterjet is subject to a power loss. This prevents faults from quickly escalating into a complete failure of the waterjet due to excessive power demands. Two simulated case studies including zig-zag path following and sideways movements are presented to demonstrate the effectiveness of the fault tolerant control thrust allocation strategy. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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18 pages, 5358 KiB

Open AccessArticle

Predicting the Motion of a USV Using Support Vector Regression with Mixed Kernel Function

by Pengfei Xu, Qingbo Cao, Yalin Shen, Meiya Chen, Yanxu Ding and Hongxia Cheng

J. Mar. Sci. Eng. 2022, 10(12), 1899; https://doi.org/10.3390/jmse10121899 - 05 Dec 2022

Cited by 1 | Viewed by 1156

Abstract

Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed kernel function (MK-SVR) combined with the [...] Read more.

Predicting the maneuvering motion of an unmanned surface vehicle (USV) plays an important role in intelligent applications. To more precisely predict this empirically, this study proposes a method based on the support vector regression with a mixed kernel function (MK-SVR) combined with the polynomial kernel (PK) function and radial basis function (RBF). A mathematical model of the maneuvering of the USV was established and subjected to a zig-zag test on the DW-uBoat USV platform to obtain the test data. Cross-validation was used to optimize the parameters of SVR and determine suitable weight coefficients in the MK function to ensure the adaptive adjustment of the proposed method. The PK-SVR, RBF-SVR, and MK-SVR methods were used to identify the dynamics of the USV and build the corresponding predictive models. A comparison of the results of the predictions with experimental data confirmed the limitations of the SVR with a single kernel function in terms of forecasting different parameters of motion of the USV while verifying the validity of the MK-SVR based on data collected from a full-scale test. The results show that the MK-SVR method combines the advantages of the local and global kernel functions to offer a better predictive performance and generalization ability than SVR based on the nuclear kernel function. The purpose of this manuscript is to propose a novel method of dynamics identification for USV, which can help us establish a more precise USV dynamic model to design and verify an excellent motion controller. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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18 pages, 831 KiB

Open AccessArticle

Adaptive Proportional-Integral Sliding Mode-Based Fault Tolerant Control for Autonomous Underwater Vehicles with Thrusters Saturation and Potential Failure

by Jian Xu, Xing Wang, Ping Liu and Qiaoyu Duan

J. Mar. Sci. Eng. 2022, 10(11), 1614; https://doi.org/10.3390/jmse10111614 - 01 Nov 2022

Cited by 3 | Viewed by 1152

Abstract

This paper focuses on the fault tolerant control of autonomous underwater vehicles (AUVs) in the presence of dynamic uncertainties and potential thruster failure issues. For this, an adaptive proportional-integral sliding mode-based fault tolerant control (APISM-FTC) is proposed to drive the AUV to follow [...] Read more.

This paper focuses on the fault tolerant control of autonomous underwater vehicles (AUVs) in the presence of dynamic uncertainties and potential thruster failure issues. For this, an adaptive proportional-integral sliding mode-based fault tolerant control (APISM-FTC) is proposed to drive the AUV to follow the desired trajectory, in the event of unknown thrusters failure and thrusters saturation. Radial basis function neural network (RBFNN) and an adaptive approach are used to evaluate the dynamics uncertainty during the construction of the APISM-FTC controller. To guarantee that all tracking errors asymptotically converge to zero, a comprehensive theoretical analysis and mathematical proof based on Lyapunov stability analysis are implemented. The simulation experiments on two fault conditions are carried out, respectively, and the control effects under normal conditions are compared. It can be shown that the designed APISM-FTC method can make the system reach a stable state quickly, and can still have a good control performance in the case of the failure of the thruster. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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17 pages, 1782 KiB

Open AccessArticle

Formation Control of Unmanned Surface Vehicles Using Fixed-Time Non-Singular Terminal Sliding Mode Strategy

by Meng Joo Er and Zhongkun Li

J. Mar. Sci. Eng. 2022, 10(9), 1308; https://doi.org/10.3390/jmse10091308 - 15 Sep 2022

Cited by 1 | Viewed by 1364

Abstract

Unmanned surface vehicles (USVs) have been widely applied in the fields of marine hydrological exploration, marine resource exploration, area search, target detection, and military operations. In order to meet the demand of a complex ocean environment, USVs are frequently grouped together to improve [...] Read more.

Unmanned surface vehicles (USVs) have been widely applied in the fields of marine hydrological exploration, marine resource exploration, area search, target detection, and military operations. In order to meet the demand of a complex ocean environment, USVs are frequently grouped together to improve the reliability of mission accomplishment. In this paper, a fixed-time control strategy, combined with a non-singular terminal sliding mode, is proposed for the formation control of USVs under complex external disturbances and system uncertainties. The main contributions of this paper are: (1) the leader–follower formation control framework is divided into a tracking control subsystem and a formation control subsystem. A new fixed-time non-singular terminal sliding mode (FTNTSM) strategy is developed for the tracking control subsystem, which dramatically increases the convergence rate and ensures closed-loop fixed-time stability; (2) a finite-time uncertain observer (FUO) is designed to observe lumped uncertainty items, which greatly increase the stability and robustness of the formation system; (3) the FUO-based fixed-time formation control (FUOFT-FC) strategy is designed for the formation control subsystem, which ensures the fast and stable formation of USVs. Fixed-time convergence of the formation system is established by Lyapunov stability analysis. Rigorous simulation and comparative studies demonstrate that the proposed method is superior to the state-of-the-art methods. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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29 pages, 4030 KiB

Open AccessArticle

Three-Dimensional Trajectory Tracking for a Heterogeneous XAUV via Finite-Time Robust Nonlinear Control and Optimal Rudder Allocation

by Yingkai Xia, Zhemin Huang, Kan Xu, Guohua Xu and Ye Li

J. Mar. Sci. Eng. 2022, 10(9), 1297; https://doi.org/10.3390/jmse10091297 - 14 Sep 2022

Cited by 2 | Viewed by 1179

Abstract

This paper proposes a novel three-dimensional trajectory tracking control methodology for a heterogeneous X-rudder autonomous underwater vehicle (XAUV) that can achieve finite-time convergence, complex actuator dynamics handling, and energy-efficient optimized rudder allocation. Under a compound robust control scheme, the trajectory tracking problem is [...] Read more.

This paper proposes a novel three-dimensional trajectory tracking control methodology for a heterogeneous X-rudder autonomous underwater vehicle (XAUV) that can achieve finite-time convergence, complex actuator dynamics handling, and energy-efficient optimized rudder allocation. Under a compound robust control scheme, the trajectory tracking problem is decomposed into three sub-problems: kinematics control, dynamics control, and rudder allocation. For kinematics control, a novel finite-time line-of-sight (FTLOS) guidance law is proposed, which can achieve faster position and orientation tracking when compared with classical LOS guidance, and is rarely studied in the existing finite time control methods. In the dynamics control loop, global finite-time terminal sliding mode control (FTTSMC) laws are provided to solve the heading control, pitching control, and surge velocity tracking control problems, where finite-time convergence is achieved in both the approaching stage and sliding mode holding stage. The multi-source uncertainties with unknown upper bounds in both kinematics and dynamics loops are well treated by finite-time extended disturbance observers (FTEDOs), thus ensuring the system robustness. Moreover, the influence of complex actuator dynamics is fully considered by employing a RBFNN compensator to deal with the propeller saturation and proposing an energy-efficient optimal rudder allocator to tackle the multi-objective and multi-constraint heterogeneous X-rudder angle assignment problem. Finally, simulation verifications are carried out for two different scenarios, where Case 1 focuses on the adaptability of the algorithm to different conditions and Case 2 focuses on the superiority of the algorithm over three other commonly used algorithms. The comparative simulation results show that the proposed controller has good adaptability to different initial and disturbance conditions, and performs better than three other classical controllers, especially in convergence speed, tracking accuracy, stability, and energy consumption. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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18 pages, 4340 KiB

Open AccessArticle

Fixed-Time Formation Control for Unmanned Surface Vehicles with Parametric Uncertainties and Complex Disturbance

by Helong Shen, Yong Yin and Xiaobin Qian

J. Mar. Sci. Eng. 2022, 10(9), 1246; https://doi.org/10.3390/jmse10091246 - 06 Sep 2022

Cited by 2 | Viewed by 1782

Abstract

In this paper, under parametric uncertainties and complex disturbances, a leader–follower formation control strategy based on accurate disturbance observer (ADO) and a novel fixed-time fast terminal sliding mode (FTFTSM) control for unmanned surface vehicles (USVs) is proposed. The main contributions of this paper [...] Read more.

In this paper, under parametric uncertainties and complex disturbances, a leader–follower formation control strategy based on accurate disturbance observer (ADO) and a novel fixed-time fast terminal sliding mode (FTFTSM) control for unmanned surface vehicles (USVs) is proposed. The main contributions of this paper are: (1) A novel fixed-time fast terminal sliding mode tracking control (FTFTSM-TC) strategy is designed for the tracking control subsystem, which greatly improves the convergence rate of the leader USV in trajectory tracking. (2) An ADO is designed to observe lumped disturbances with the smallest approximation error. The ADO greatly reduces the interference of disturbances and improves the performance of the formation system. (3) An ADO-based fixed-time formation control (ADO-FTFC) strategy is developed for the formation control subsystem to maintain the desired formation. Stability of the formation control system is established by the Lyapunov theory. Simulation results show that the proposed control strategy is superior for the USVs formation control. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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16 pages, 1475 KiB

Open AccessArticle

Adaptive Formation Control of Multiple Underactuated Autonomous Underwater Vehicles

by Ji-Hong Li, Hyungjoo Kang, Min-Gyu Kim, Mun-Jik Lee, Gun Rae Cho and Han-Sol Jin

J. Mar. Sci. Eng. 2022, 10(9), 1233; https://doi.org/10.3390/jmse10091233 - 02 Sep 2022

Cited by 3 | Viewed by 1873

Abstract

In this paper, we present a 3D formation control scheme for a group of torpedo-type underactuated autonomous underwater vehicles (AUVs). These multiple AUVs combined with an unmanned surface vessel (USV) construct a sort of star-topology acoustic communication network where the USV is at [...] Read more.

In this paper, we present a 3D formation control scheme for a group of torpedo-type underactuated autonomous underwater vehicles (AUVs). These multiple AUVs combined with an unmanned surface vessel (USV) construct a sort of star-topology acoustic communication network where the USV is at the center point. Due to this kind of topological feature, this paper applies a virtual school concept. This is a geometric graph where each node is taken as a virtual leader for each specific AUV and assigned its own reference trajectory. For each individual vehicle, its formation strategy is simple: just follow the trajectory of its corresponding virtual leader so as for multiple AUVs to compose the given formation. As for the formation subject, this paper mainly focuses on the formation tracking problem rather than the formation producing. For the torpedo-type vehicle considered in this paper, there are only three control inputs (surge force, pitch, and yaw moments) available for its underwater 3D motion and therefore this is a typical underactuated system. For the following vehicle’s trajectory, a sort of potential field method is used for obstacle avoidance, and a neural network-based adaptive scheme is applied to on-line approximate the vehicle’s unknown nonlinear dynamics, and the uncertainty terms including modeling errors, measurement noises, and external disturbances are handled by the properly designed robust scheme. The proposed formation method can guarantee the uniform ultimate boundedness (UUB) of the closed-loop system. Numerical studies are also carried out to verify the effectiveness of the proposed scheme. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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15 pages, 3796 KiB

Open AccessArticle

Local Defogging Algorithm for the First Frame Image of Unmanned Surface Vehicles Based on a Radar-Photoelectric System

by Qingze Yu and Yumin Su

J. Mar. Sci. Eng. 2022, 10(7), 969; https://doi.org/10.3390/jmse10070969 - 14 Jul 2022

Cited by 1 | Viewed by 1358

Abstract

Unmanned surface vehicles frequently encounter foggy weather when performing surface object tracking tasks, resulting in low optical image quality and object recognition accuracy. Traditional defogging algorithms are time consuming and do not meet real-time requirements. In addition, there are problems with oversaturated colors, [...] Read more.

Unmanned surface vehicles frequently encounter foggy weather when performing surface object tracking tasks, resulting in low optical image quality and object recognition accuracy. Traditional defogging algorithms are time consuming and do not meet real-time requirements. In addition, there are problems with oversaturated colors, low brightness, and overexposed areas in the sky. In order to solve the problems mentioned above, this paper proposes a defogging algorithm for the first frame image of unmanned surface vehicles based on a radar-photoelectric system. The algorithm involves the following steps. The first is the fog detection algorithm for sea surface image, which determines the presence of fog. The second is the sea-sky line extraction algorithm which realizes the extraction of the sea-sky line in the first frame image. The third is the object detection algorithm based on the sea-sky line, which extracts the target area near the sea-sky line. The fourth is the local defogging algorithm, which defogs the extracted area to obtain higher quality images. This paper effectively solves the problems above in the sea test and dramatically reduces the calculation time of the defogging algorithm by 86.7%, compared with the dark channel prior algorithm. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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Review

Jump to: Research

27 pages, 2480 KiB

Open AccessReview

A Review of Autonomous Tugboat Operations for Efficient and Safe Ship Berthing

by Jun-Hyuk Choi, Ju-Yeong Jang and Joohyun Woo

J. Mar. Sci. Eng. 2023, 11(6), 1155; https://doi.org/10.3390/jmse11061155 - 31 May 2023

Cited by 3 | Viewed by 2859

Abstract

Autonomous ship technology, which includes real-time monitoring, satellite communication, and automatic navigation, is rapidly advancing. Despite significant research on single unmanned ships, there is a lack of studies on complex tasks, such as ship berthing using a swarm of autonomous tugboats. This review [...] Read more.

Autonomous ship technology, which includes real-time monitoring, satellite communication, and automatic navigation, is rapidly advancing. Despite significant research on single unmanned ships, there is a lack of studies on complex tasks, such as ship berthing using a swarm of autonomous tugboats. This review article provides an overview of various projects related to autonomous tugboats for ship berthing and discusses the research trends in the required technologies, including recognition, decision making, modeling, and control. We identify the areas that have been underexplored in existing studies and suggest future research directions to advance the field. Overall, this review contributes to a better understanding of the challenges and opportunities for the development of autonomous tugboats for ship berthing. Full article

(This article belongs to the Special Issue Advanced Studies in the Autonomy and Control of Marine Vehicle Systems)

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