Sliding-Mode Control of STENA DRILLMAX Drillship with Environmental Disturbances for Dynamic Positioning

Chin, Cheng Siong; Lio, C. S.

doi:10.1007/978-981-15-0474-7_10

Cited by 2 publications

(8 citation statements)

References 16 publications

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“…, 24} in (15) and (16) respectively. Also tapped delay lines (TDL) values Tdy and Tdx for the NARX inputs which we have used for training the weights in (13)- (17) as shown in Figure 7 with daily DP load forecasting as 24, 48, 72, hourly from 1 to 24, and weekly 168 have been selected, respectively.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…In (17), ∇y n (w ij ) T and ∇ 2 y n (w ij ) are the vector of gradient and the error function of the hessian matrix, hence the dw ij is defined as:…”

Section: Recurrent Dynamic Neural Networkmentioning

confidence: 99%

“…where ∇ 2 y n (w ij ) = J T J + S. The J is specified as the Jacobian matrix, which includes the initial network derived signal error, which is related to adjust the weights of the neuron. By considering the directional propagation algorithm, (17) is computed in (17), where S expresses the second-order derivative function ∇ 2 y n (w ij ). By ignoring the S in (17), the equation is solved by the Gauss-Newton method.…”

Section: Recurrent Dynamic Neural Networkmentioning

confidence: 99%

“…By considering the directional propagation algorithm, (17) is computed in (17), where S expresses the second-order derivative function ∇ 2 y n (w ij ). By ignoring the S in (17), the equation is solved by the Gauss-Newton method. We can also obtain results for the hessian matrix by the following approximation in (19).…”

Section: Recurrent Dynamic Neural Networkmentioning

confidence: 99%

“…In this evaluation with a ship model, the filter supervisor eliminates noises from the measurement of the position speed of the ship by applying a PD controller that changes gradually because of environmental variations. To decrease the deviation of vessels station from the set point while ecological disturbances cause unexpected sudden changes of position, an adaptive control technique is developed to approximate nonlinear DPS parameters with fuzzy logic theory or neural network (NN) in [11][12][13][14][15][16][17][18][19][20]. As a result, an adaptive NN based on a fuzzy inference system is proposed in [21][22][23], which encloses a fuzzy logic to adapt the NN weights for the period of learning procedure by using fuzzy rules.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

Mehrzadi

Terriche

et al. 2020

Applied Sciences

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The dynamic positioning (DP) system is a progressive technology, which is used in marine vessels and maritime structures. To keep the ship position from displacement in operation mode, its thrusters are used automatically to control and stabilize the position and heading of vessels. Hence, the DP load forecasting is already an essential part of DP vessels, which the DP power demand from the power management system (PMS) for thrusting depends on weather conditions. Furthermore, the PMS is used to control power generation, and prevent power failure, limitation. To perform station keeping of vessels by DPS in environmental changes such as wind, waves, capacity, and reliability of the power generators. Hence, a lack of power may lead to lower DP performance, loss of power, and position, which is called shutdown. Therefore, precise DP power demand prediction for maintaining the vessel position can provide the PMS with sufficient information for better performance in a complex decision-making process for the DP vessel. In this paper, the concept of deep learning techniques is introduced into DPS for DP load forecasting. A Levenberg–Marquardt algorithm based on a nonlinear recurrent neural network is employed in this paper for predicting thrusters’ power consumption in sea state variations due to challenges in power generation with the relative degree of accuracy by combining weather parameter dependencies as environmental disturbances. The proposed method evaluates with three traditional forecasting methods through a set of practical real-time DP load and weather parametric data. Numerical analysis has shown that with the proposed method, the future DP load behavior can be predicted more accurately than that obtained from the traditional methods, which greatly assists in operation and planning of power system to maintain system stability, security, reliability, and economics.

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Section: The Proposed Methodsmentioning

confidence: 99%

“…In (17), ∇y n (w ij ) T and ∇ 2 y n (w ij ) are the vector of gradient and the error function of the hessian matrix, hence the dw ij is defined as:…”

Section: Recurrent Dynamic Neural Networkmentioning

confidence: 99%

Section: Recurrent Dynamic Neural Networkmentioning

confidence: 99%

Section: Recurrent Dynamic Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

Mehrzadi

Terriche

et al. 2020

Applied Sciences

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Determination of Anchor Drop Sequence during Vessel Anchoring Operations Based on Expert Knowledge Base and Hydrometeorological Conditions

Wnorowski,

Łebkowski

2023

Electronics

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Presently, the most common technique for maintaining a ship’s location is dynamic positioning, which uses a series of thrusters to hold its position. This method is resilient to moderate hydro-meteorological conditions, eliminating the need for extensive preliminary steps before initiating positioning operations. An alternative approach involves station keeping using a set of anchors, where thrusters are not employed, necessitating careful planning of the anchorage in light of hydro-meteorological conditions. Presently, in vessels using this anchoring method, the captain determines the order of anchor drops, taking into account the prevailing weather conditions, the ship’s maneuvering abilities, and vessel capability plots. This article introduces a novel algorithm that uses sensor-acquired weather data and a cognitive knowledge base to establish the best sequence for anchor drops. This innovation represents a significant stride towards the automation of the anchoring process. By using the anchorage planning algorithm presented in this publication, it has been possible to reduce the time required for anchor deployment by about 52%, due to the preparation of the anchor deployment strategy in port. A reduction in energy consumption of about 8% was also achieved.

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Sliding-Mode Control of STENA DRILLMAX Drillship with Environmental Disturbances for Dynamic Positioning

Cited by 2 publications

References 16 publications

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

A Deep Learning Method for Short-Term Dynamic Positioning Load Forecasting in Maritime Microgrids

Determination of Anchor Drop Sequence during Vessel Anchoring Operations Based on Expert Knowledge Base and Hydrometeorological Conditions

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