Dynamic Positioning Research Articles

Dynamic Positioning for Autonomous Underwater Vehicles: A Tube Model Predictive Control approach

Dynamic Positioning for Autonomous Underwater Vehicles: A Tube Model Predictive Control approach

Automatic docking with extended dynamic positioning

Automatic docking with extended dynamic positioning

Analysis and improvement of power quality in the onboard electrical power systems within a self-propelled floating crane

This research addresses electromagnetic compatibility and power quality (EMC and PQ) challenges in shipboard electric power systems (SEPS) with dynamic positioning (DP) capabilities, focusing on the self-propelled floating crane (SPFC) “EXPERT 3.” Our goal is to enhance methodologies for evaluating power quality indicators (PQI) related to voltage and current waveform distortions, specifically in converter-based propulsion complexes (CBPCs) featuring variable-frequency regulated asynchronous drives (VFDs). We investigate harmonic current and voltage distortions, reactive power generation, and power factor (PF) in SEPS “EXPERT 3″ during the operation of VFDs with conventional semiconductor power converters (SPCs). A refined MATLAB model considers equipment and cable network parameters, validating distortion indicators at ship synchronous generators (SGs). PQI compliance with international maritime standards is verified at the point of common coupling (PCC) for various VFD configurations with pulse width modulated (PWM) SPCs and filter-compensating devices (FCDs). We propose an improved controlled FCD (CFCD) incorporating a resonance filter (RF) and a controllable reactor compensator (CRC). CFCD efficiency in various VFD configurations is experimentally validated. We analyze harmonic distortions of voltage and current, including high-frequency (HF) commutations in VFDs with input thyristor-based rectifiers (TBRs), considering parasitic ”phase-hull“ capacitances of cable line segments (CLS). Our study examines harmonics in CBPC-based SEPS in two sub-frequency ranges: low-frequency (LF) − 0 to 2 kHz and high-frequency (HF) − 2 kHz to 500 kHz.© 2017 Elsevier Inc. All rights reserved.

Robust adaptive control for dynamic positioning vessels with actuator faults via the multiport event-triggered mechanism

This paper presents a robust adaptive neural control algorithm for dynamic positioning tasks of full-actuated vessels using a novel multiport event-triggered mechanism. The proposed mechanism only updates control signals and feedback error signals upon violation of the multiport event-triggered rule. Therefore, the communication loads between the sensors and controller, as well as between the controller and actuators, are significantly mitigated. Furthermore, a fault-adaptive method is adopted by estimating the uncertain actuator faults, based on the proposed event-triggered mechanism, the fault-adaptive parameters will be updated at the triggering instant, eliminating the destabilizing effects of the closed-loop system. Besides, the dynamic surface control (DSC) and the robust neural damping technique are employed to address the problems of “complexity explosion” and system uncertainties. Based on Lyapunov stability theory, the closed-loop system is proved to satisfy semi-globally uniform ultimate bounded (SGUUB). Finally, a simulation experiment validates the effectiveness of the proposed algorithm.

A Dynamic Position Embedding-Based Model for Student Classroom Complete Meta-Action Recognition.

The precise recognition of entire classroom meta-actions is a crucial challenge for the tailored adaptive interpretation of student behavior, given the intricacy of these actions. This paper proposes a Dynamic Position Embedding-based Model for Student Classroom Complete Meta-Action Recognition (DPE-SAR) based on the Video Swin Transformer. The model utilizes a dynamic positional embedding technique to perform conditional positional encoding. Additionally, it incorporates a deep convolutional network to improve the parsing ability of the spatial structure of meta-actions. The full attention mechanism of ViT3D is used to extract the potential spatial features of actions and capture the global spatial-temporal information of meta-actions. The proposed model exhibits exceptional performance compared to baseline models in action recognition as observed in evaluations on public datasets and smart classroom meta-action recognition datasets. The experimental results confirm the superiority of the model in meta-action recognition.

Anthropometric survey of dynamic hand dimensions: A pilot study

IntroductionEffective glove design requires understanding hand dimensions and movement. Designers must study hands in static and dynamic postures. They do this to identify key movement points. This study aimed to determine hand size during motion. The goal was to understand and specify how hand shape changes. Methods and materialsThirty dynamic hand dimensions of 100 Iranian males were measured in six postures using photography and three-dimensional modeling. The data were classified and compared based on hand positions. ResultsThe results showed significant differences in hand size during movements. Four hand.dimensions were found to have significant differences (p > 0.05) related to hand posture. Sizes either decreased or increased significantly. The dorsal hand length underwent a significant alteration. This was compared to the palmar hand length during movement. ConclusionThe results of this study help to design gloves with optimal fit, performance and comfort. The results show that the dorsal hand length should be increased, while the palmar hand length should be decreased in the design of the glove pattern. Gloves that are currently made based on static postures cannot provide a good fit. Attention should be paid to the measurement of the hand in dynamic positions.

Dynamic position optimization of submerged array square jet cooling in confined space for chip thermal dissipation

Jet position has a significant influence on the heat transfer characteristics of the chip. Therefore, it is necessary to optimize the jet position to improve its operating performance. Besides, traditional and typical optimization method is to establish abundant experimental/numerical models, which is very complicated and time-consuming, and the accuracy and effectiveness of results still need to be verified. Hence, a dynamic optimization method is proposed in this study and the jet position is directly controlled during the simulation process based on dynamic regulation of user-defined functions, which is directly related to the real-time physical quantities and the user-defined optimization strategy. Results show that compared to traditional jet position, the maximum temperature and temperature dispersion for the optimal jet position decrease by an average of 25.20 °C and 69.88 %, respectively. Furthermore, the optimal jet position efficiently achieves the balance between thermal performance and power consumption. To sum up, compared with manual optimization method, the dynamic optimization method proposed in this study only needs to establish one model and simulate it once to obtain the optimized result. This process is very convenient, high-efficiency and less time-consuming, which will be especially beneficial to effectively enhance the performance of the chip thermal dissipation.

Dynamic positioning system of ships with RBF neural network compensator

Abstract In Dynamic Positioning, traditional control methods lack stability, while neural networks and reinforcement learning have become research hotspots. By learning decision-making strategies from environmental observation data, we can adapt to environmental changes in real time and achieve optimal control effects. We also design a reward function to evaluate control performance. Dynamic inversion control is a control strategy that transforms nonlinear systems into linear systems. To address the issue of ship dynamic positioning, we propose a novel controller design method by combining RBF neural networks with dynamic inversion control. Its effectiveness has been verified through simulation experiments, demonstrating its potential and value in practical applications. This method will help improve the positioning accuracy of ships and optimize control performance.

Application Of Swot Analysis To The Sustainability Of As-Syams Boutique Business In Makassar City

The development of fashion trends cannot be separated from increasingly trendy technology, so business actors are trying to utilize these technological developments as a means of ideas and innovation to create products that are appropriate to the times so that they can attract the attention of consumers and be able to compete in the market. Therefore, every business person is required to know the right strategy for introducing the products they produce. This research aims to find out the SWOT implementation strategy carried out by the As-Syams boutique business in Makassar City. This research is descriptive research using a qualitative approach. The informant was determined deliberately, namely the owner of the As-Syams boutique business. Data collection used in research includes observation, interviews, and documentation. The data obtained was analyzed using SWOT analysis. Based on the research results, show that the strategy implemented by the As-Syams boutique business is in quadrant one, namely Aggressive (growth-oriented strategy) or in a dynamic growth position or in a strength-opportunity strategy position (using strengths to take advantage of opportunities in decision-making). This shows that there is an opportunity to develop the As-Syams boutique business, apart from having strengths that are greater than its weaknesses, it also has opportunities that are greater than threats.

Prescribed-time active fault-tolerant control of dynamic positioning vessels based on improved iterative learning observer

Considering the dynamic positioning (DP) vessel control system with actuator faults and unknown environment disturbances, a fault-tolerant control scheme based on improved iterative learning observer (ILO) is proposed in this article. Firstly, the kinematics and dynamics models of DP vessels are established containing the actuator faults and environmental disturbances. Secondly, an improved ILO is designed to handle the unknown faults and disturbances concurrently with higher estimation precision by introducing the state information of the previous moment. Further, a prescribed-time active fault-tolerant control scheme is presented on the basis of the improved ILO and prescribed-time stability. And the stability of proposed ILO and DP fault-tolerant control system are analyzed based on Lyapunov theory. Finally, the effectiveness and advantages of the presented ILO and active fault-tolerant control scheme are illustrated by numerical simulations and comparisons.

Unmanned Floating Object Transportation Applying Multiple Dynamic Positioning Tugboats: A Novel Cooperative Control Algorithm

Abstract To promote the automation and intelligence of marine floating object transportation, this paper proposes a cooperative consensus control algorithm for multiple unmanned dynamic positioning (DP) tugboats to transport an unactuated floating structure object using cables. The major concept of present algorithm is to implement a leader-follower formation strategy among distributed tugboats, with the aim of effectively maneuvering the object by adjusting the relative position between the virtual leader and the object in a reasonable manner. A cooperative consensus controller is integrated into the feedback controller of each tugboat local DP system in order to facilitate cooperative coordination among tugboats in a formation. A customized DP system, which incorporates the radial basis function neural network supervisor control into the DP controller design and introduces nonlinear disturbance observer into the low-frequency motion state observer design, is proposed to address cable tension disturbances, and thus, the tracking performance of the DP tugboat is improved. Full-scale nonlinear time domain simulations are performed under variable environmental conditions, verifying the effectiveness of the proposed automatic towing control scheme.

Research on dynamic positioning of navigation system using K nearest neighbor algorithm and bayesian fusion based on visible light communication

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m×5 m×4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m×1 m×1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Fault‐tolerant control of unmanned marine vehicles with unknown parametric dynamics via integral sliding mode output feedback control

AbstractThis paper focuses on fault‐tolerant control (FTC) for unmanned marine vehicles (UMVs) subject to unknown parametric dynamics, thruster faults, and external disturbances. A novel FTC strategy based on the integral sliding mode output feedback method is proposed. Based on a high gain compensator and output information, a novel integral sliding mode fault‐tolerant controller is constructed to guarantee the dynamic positioning (DP) of UMVs. An attraction region that is related to the known positive constants with respect to dynamic uncertainties has been revealed for the first time. Finally, the closed‐loop stability can be guaranteed from the every initial time in despite of unknown parametric dynamics, thruster faults, and external disturbances. Simulation though a typical floating production ship model has verified the effectiveness of the proposed method.

The Evolution of Maritime Technology Development: A Dynamic Positioning System Perspective of Maritime Autonomous Surface Ship

The Evolution of Maritime Technology Development: A Dynamic Positioning System Perspective of Maritime Autonomous Surface Ship

IRBEVF-Q: Optimization of Image-Radar Fusion Algorithm Based on Bird's Eye View Features.

In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird's Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy.

Short-Term Electricity Load Forecasting Based on Improved Data Decomposition and Hybrid Deep-Learning Models

Short-term power load forecasting plays a key role in daily scheduling and ensuring stable power system operation. The problem of the volatility of the power load sequence and poor prediction accuracy is addressed. In this study, a learning model integrating intelligent optimization algorithms is proposed, which combines an ensemble-learning model based on long short-term memory (LSTM), variational modal decomposition (VMD) and the multi-strategy optimization dung beetle algorithm (MODBO). The aim is to address the shortcomings of the dung beetle optimizer algorithm (DBO) in power load forecasting, such as its time-consuming nature, low accuracy, and ease of falling into local optimum. In this paper, firstly, the dung beetle algorithm is initialized using a lens-imaging reverse-learning strategy to avoid premature convergence of the algorithm. Second, a spiral search strategy is used to update the dynamic positions of the breeding dung beetles to balance the local and global search capabilities. Then, the positions of the foraging dung beetles are updated using an optimal value bootstrapping strategy to avoid falling into a local optimum. Finally, the dynamic-weighting coefficients are used to update the position of the stealing dung beetle to improve the global search ability and convergence of the algorithm. The proposed new algorithm is named MVMO-LSTM. Compared to traditional intelligent algorithms, the four-quarter averages of the RMSE, MAE and R2 of MVMO-LSTM are improved by 0.1147–0.7989 KW, 0.09799–0.6937 KW, and 1.00–13.05%, respectively. The experimental results show that the MVMO-LSTM proposed in this paper not only solves the shortcomings of the DBO but also enhances the stability, global optimization capability and information utilization of the model.

Multi-Objective Optimization for Thrust Allocation of Dynamic Positioning Ship

Thrust allocation (TA) plays a critical role in the dynamic positioning system (DPS). The task of TA is to allocate the rotational speed and angle of each thruster to generate the generalized control forces. Most studies take TA as a single-objective optimization problem; however, TA is a multi-objective optimization problem (MOP), which needs to satisfy multiple conflicting allocation objectives simultaneously. This study proposes an improved multi-objective particle swarm optimization (IMOPSO) method to deal with the non-convex MOP of TA. The objective functions of reducing the allocation error, and minimizing the power consumption and the tear-and-wear of thrusters under physical constraints, are established and solved via MOPSO. To enhance the global seeking ability, the improved mutation strategy combined with the roulette wheel mechanism is adopted. It is shown through test data that IMOPSO converges better than multi-objective algorithms such as MOPSO and nondominated sorting genetic algorithm II (NSGA-II). Simulations are conducted for a DP ship with two propeller–rudder combinations. The simulation results with the single-objective PSO algorithm show that the proposed IMOPSO algorithm reduces thrust allocation errors in the three directions of surge, sway, and yaw by 48.48%, 39.64%, and 15.02%, respectively, and reduces power consumption by 44.53%, which demonstrates the feasibility and effectiveness of the proposed method.

Robust Fixed-Time Adaptive Fault-Tolerant Control for Dynamic Positioning of Ships with Thruster Faults

The ship dynamic positioning system has long been a subject of great interest due to the intricate and unpredictable nature of the maritime environment, which affects its dependability and timeliness. This study focuses on the issue of developing a robust, efficient, and adaptable fault-tolerant control system for ship dynamic positioning systems that experience thruster malfunctions. Firstly, we build the motion model of the ship’s dynamic positioning system with three degrees of freedom, as well as the model for faults in the thruster. A second-order fast fixed-time nonsingular sliding mode surface is formulated, and a robust adaptive control technique is introduced to counteract external environmental disturbances and model errors. The objective is to ensure rapid convergence of the system within a predetermined time frame. Simultaneously, combined with the fault-tolerant control technique, it is ensured that the dynamic positioning ship can still achieve system stability when thruster faults occur. In addition, the analysis of the Lyapunov stability criterion proves that the proposed fixed-time adaptive fault-tolerant control scheme can make the system tracking error converge within a fixed time. Finally, the effectiveness of the proposed control scheme is verified by numerical simulation results.

Four-quadrant propeller hydrodynamic performance mapping for improving ship motion predictions

On the path toward fully autonomous sea vessels, forecasting a ship’s exact velocity and position during its route plays a crucial role in dynamic positioning, target tracking, and autopilot operations of the unmanned body navigating toward predetermined locations. This paper addresses the prediction of the operational performance of a free-running submarine advancing in a straight route (in surge motion). Along with the forward advancing vessel (straight-ahead motion) the study covers all possible scenarios of ship’s surge, including crash-ahead, crash-back, and astern motions. Conventional maneuvering models cannot handle motions other than forward advancement due to the absence of propeller data in all four quadrants of hydrodynamic performance map. This study proposes an approach for predicting submarine performance in all these surge conditions by utilizing four-quadrant propeller performance and resistance test data. We developed an in-house code, SMot4QP, to simulate ship speed and position in the time domain. We obtained satisfying results for the straight-ahead and crash-ahead motions, while the crash-back and astern maneuvers require further refinement due to propeller wake interaction with the hull. The proposed method is capable of predicting the motions of all types of vessels using the ship’s resistance and four-quadrant propeller test results. Thus, SMot4QP offers a fast and robust alternative to computationally expensive free-running self-propulsion simulations for operational performance prediction in broader naval applications.

Numerical method determining the time-dependent position of a minority phase sphere in the Fe-Cu alloy droplet

Abstract A numerical method was developed to track the dynamic positions of the L2 phase sphere inside the Fe-Cu alloy droplet. Firstly, the temperature field distribution within alloy droplet was calculated, and the variation in thermal gradient was analyzed. Secondly, the migration behavior of the sphere was analyzed based on Marangoni convection. Finally, the theoretical position of the 10 μm sphere was determined. The results demonstrated that there is a little difference in temperature between the center and the surface of the droplet, but its gradient is very different. Moreover, the maximum migration times for the L2 phase sphere were determined to be ~0.02 s, 0.07 s, and 0.14 s within 300μm, 600μm, and 900μm alloy droplets, respectively. In addition, it was also found that the same-sized spheres can reach the same relative position, i.e., 0.485R. This means the final position of the sphere within the different alloy droplets is independent of the droplet size.