Optimized Multi-cascade Fuzzy Model for Ship Dynamic Positioning System Based on Genetic Algorithm

Do, Viet-Dung; Dang, Xuan-Kien; Huynh, Leminh Thien; Cừu, Hồ Văn

doi:10.1007/978-3-030-30149-1_14

Cited by 12 publications

(8 citation statements)

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“…Hence, drives are the most critical elements of DP vessels and can have time lag impact and, therefore, it is essential to resolve the time delay There are some membership functions allocated to fuzzy subsets rules such as NS (negative small), ZZ (zero), and SP (small positive), even though the Trappist membership functions are subdivided into fuzzy subgroups: NM (negative medium) and PM (positive medium). The works in [67][68][69][70][71][72][73][74][75][76][77][78][79] presented the fuzzification membership function for fuzzy controlling in DP vessels. The output membership degrees and linguistic rules cannot be used straightly.…”

Section:  Post Processingmentioning

confidence: 99%

Review of Dynamic Positioning Control in Maritime Microgrid Systems

Mehrzadi

Terriche

et al. 2020

Energies

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For many offshore activities, including offshore oil and gas exploration and offshore wind farm construction, it is essential to keep the position and heading of the vessel stable. The dynamic positioning system is a progressive technology, which is extensively used in shipping and other maritime structures. To maintain the vessels or platforms from displacement, its thrusters are used automatically to control and stabilize the position and heading of vessels in sea state disturbances. The theory of dynamic positioning has been studied and developed in terms of control techniques to achieve greater accuracy and reduce ship movement caused by environmental disturbance for more than 30 years. This paper reviews the control strategies and architecture of the DPS in marine vessels. In addition, it suggests possible control principles and makes a comparison between the advantages and disadvantages of existing literature. Some details for future research on DP control challenges are discussed in this paper.

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Section:  Post Processingmentioning

confidence: 99%

Review of Dynamic Positioning Control in Maritime Microgrid Systems

Mehrzadi

Terriche

et al. 2020

Energies

View full text Add to dashboard Cite

show abstract

“…The nonlinear motion of a vessel in DPs mode is described by Fossen [38] and reused by Do et al [39]. Two separate coordinate systems presented by Fig.…”

Section: Problem Formulation a Dynamic Positioning Systemmentioning

confidence: 99%

“…In this study, we use a fuzzy Takagi-Sugeno (TS) model for the DPs of supply vessel which has a double-inputs e, de/dt, and single-output τ [39]. The inference process system combines MFs with if-then rules and the fuzzy logic operators.…”

Section: Adaptive Fuzzy Controlmentioning

confidence: 99%

“…To fairly compare the simulation results obtained, RAFC-GA and other solutions apply the same number of training parameters and vessel structure modelling. The performance of RAFC-GA model is compared with that of the fuzzy particle swarm optimization (F-PSO) [35], and the fuzzy genetic algorithm (F-GA) [39] for DPs control. This study realizes the effectiveness comparison of the various models in terms of fuzzy rules, number of parameters, training time, convergence generation and testing time.…”

Section: ) Convergence Evaluation Of Gasmentioning

confidence: 99%

“…6a is satisfied in the overshoot and fluctuation aspect. The performance comparison of related solutions, Fuzzy and F-PSO [35], F-GA [39], AFC [47] and proposed AFC-GA and RAFC-GA, showed in Table 3. In the detail, the maximum overshoots, the value of the Fuzzy, F-PSO, F-GA, AFC, AFC-GA and the RAFC-GA are 0.25m, 0.22m, 0.21m, 0.23m, 0.19m and 0.15m, respectively.…”

Section: B Performance Evaluation By Simulationmentioning

confidence: 99%

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